
R version 3.3.0 (2016-05-03) -- "Supposedly Educational"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> # Replication script for Leifeld, Philip and Dana R. Fisher: Membership 
> # Nominations in International Scientific Assessments. Nature Climate Change.
> 
> ################################################################################
> # Load packages and set random seed
> ################################################################################
> 
> library("statnet")
Loading required package: tergm
Loading required package: statnet.common
Loading required package: ergm
Loading required package: network
network: Classes for Relational Data
Version 1.13.0 created on 2015-08-31.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
                    Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Martina Morris, University of Washington
                    Skye Bender-deMoll, University of Washington
 For citation information, type citation("network").
 Type help("network-package") to get started.


ergm: version 3.7.1, created on 2017-03-20
Copyright (c) 2017, Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Carter T. Butts, University of California -- Irvine
                    Steven M. Goodreau, University of Washington
                    Pavel N. Krivitsky, University of Wollongong
                    Martina Morris, University of Washington
                    with contributions from
                    Li Wang
                    Kirk Li, University of Washington
                    Skye Bender-deMoll, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm").

NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()
constriant which distorted the sampled distribution somewhat. In
addition, Sampson's Monks datasets had mislabeled verteces. See the
NEWS and the documentation for more details.

Loading required package: networkDynamic

networkDynamic: version 0.9.0, created on 2016-01-12
Copyright (c) 2016, Carter T. Butts, University of California -- Irvine
                    Ayn Leslie-Cook, University of Washington
                    Pavel N. Krivitsky, University of Wollongong
                    Skye Bender-deMoll, University of Washington
                    with contributions from
                    Zack Almquist, University of California -- Irvine
                    David R. Hunter, Penn State University
                    Li Wang
                    Kirk Li, University of Washington
                    Steven M. Goodreau, University of Washington
                    Jeffrey Horner
                    Martina Morris, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("networkDynamic").


tergm: version 3.4.0, created on 2016-03-28
Copyright (c) 2016, Pavel N. Krivitsky, University of Wollongong
                    Mark S. Handcock, University of California -- Los Angeles
                    with contributions from
                    David R. Hunter, Penn State University
                    Steven M. Goodreau, University of Washington
                    Martina Morris, University of Washington
                    Nicole Bohme Carnegie, New York University
                    Carter T. Butts, University of California -- Irvine
                    Ayn Leslie-Cook, University of Washington
                    Skye Bender-deMoll
                    Li Wang
                    Kirk Li, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("tergm").

Loading required package: ergm.count

ergm.count: version 3.2.0, created on 2015-06-18
Copyright (c) 2015, Pavel N. Krivitsky, University of Wollongong
                    with contributions from
                    Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm.count").

NOTE: The form of the term ‘CMP’ has been changed in version 3.2 of
‘ergm.count’. See the news or help('CMP') for more information.

Loading required package: sna
sna: Tools for Social Network Analysis
Version 2.4 created on 2016-07-23.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
 For citation information, type citation("sna").
 Type help(package="sna") to get started.


statnet: version 2016.4, created on 2016-03-23
Copyright (c) 2016, Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Carter T. Butts, University of California -- Irvine
                    Steven M. Goodreau, University of Washington
                    Pavel N. Krivitsky, University of Wollongong
                    Skye Bender-deMoll
                    Martina Morris, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("statnet").


There are updates for the following statnet packages on CRAN:
           Installed ReposVer Built  
ergm       "3.6.0"   "3.7.1"  "3.3.0"
ergm.count "3.2.0"   "3.2.2"  "3.2.2"
sna        "2.3-2"   "2.4"    "3.2.3"
statnet    "2016.4"  "2016.9" "3.3.0"
Restart R and use "statnet::update_statnet()" to get the updates.
> library("xergm")
Loading required package: xergm.common

Attaching package: ‘xergm.common’

The following object is masked from ‘package:ergm’:

    gof

Loading required package: btergm
Loading required package: ggplot2
Package:  btergm
Version:  1.9.0
Date:     2017-03-30
Authors:  Philip Leifeld (University of Glasgow)
          Skyler J. Cranmer (The Ohio State University)
          Bruce A. Desmarais (Pennsylvania State University)

Loading required package: tnam
Package:  tnam
Version:  1.6.5
Date:     2017-03-31
Authors:  Philip Leifeld (University of Glasgow)
          Skyler J. Cranmer (The Ohio State University)

Loading required package: rem
Loading required package: GERGM
GERGM: Generalized Exponential Random Graph Models
Version 0.11.2 created on 2017-03-14.
copyright (c) 2017, Matthew J. Denny, Penn State University
                    James D. Wilson, University of San Francisco
                    Skyler Cranmer, Ohio State University
                    Bruce A. Desmarais, Penn State University
                    Shankar Bhamidi, University of North Carolina
Type help('gergm') to get started.
Development website: https://github.com/matthewjdenny/GERGM
Package:  xergm
Version:  1.8.2
Date:     2017-04-01
Authors:  Philip Leifeld (University of Glasgow)
          Skyler J. Cranmer (The Ohio State University)
          Bruce A. Desmarais (Pennsylvania State University)

Please cite the xergm package in your publications -- see citation("xergm").

> library("texreg")
Version:  1.36.23
Date:     2017-03-03
Author:   Philip Leifeld (University of Glasgow)

Please cite the JSS article in your publications -- see citation("texreg").
> library("xtable")
> 
> sessionInfo()  # display version numbers of packages for replication
R version 3.3.0 (2016-05-03)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] xtable_1.8-0         texreg_1.36.23       xergm_1.8.2         
 [4] GERGM_0.11.2         rem_1.1.2            tnam_1.6.5          
 [7] btergm_1.9.0         ggplot2_2.0.0        xergm.common_1.7.7  
[10] statnet_2016.4       sna_2.4              ergm.count_3.2.0    
[13] tergm_3.4.0          networkDynamic_0.9.0 ergm_3.7.1          
[16] network_1.13.0       statnet.common_3.3.0

loaded via a namespace (and not attached):
 [1] deSolve_1.12        gtools_3.5.0        lpSolve_5.6.13     
 [4] splines_3.3.0       lattice_0.20-33     mstate_0.2.8       
 [7] colorspace_1.2-6    flexsurv_0.7        stats4_3.3.0       
[10] mgcv_1.8-12         survival_2.39-4     nloptr_1.0.4       
[13] RColorBrewer_1.1-2  muhaz_1.2.6         speedglm_0.3-1     
[16] trust_0.1-7         plyr_1.8.3          robustbase_0.92-5  
[19] munsell_0.4.2       gtable_0.1.2        caTools_1.17.1     
[22] mvtnorm_1.0-5       coda_0.18-1         permute_0.8-4      
[25] parallel_3.3.0      DEoptimR_1.0-4      Rcpp_0.12.11       
[28] KernSmooth_2.23-15  ROCR_1.0-7          scales_0.4.1       
[31] gdata_2.17.0        vegan_2.3-1         RcppParallel_4.3.20
[34] lme4_1.1-10         gplots_2.17.0       grid_3.3.0         
[37] quadprog_1.5-5      tools_3.3.0         bitops_1.0-6       
[40] magrittr_1.5        RSiena_1.1-232      cluster_2.0.4      
[43] MASS_7.3-45         Matrix_1.2-6        minqa_1.2.4        
[46] boot_1.3-17         igraph_1.0.1        nlme_3.1-128       
> # R version 3.3.0 (2016-05-03)
> # Platform: x86_64-pc-linux-gnu (64-bit)
> # Running under: Ubuntu 16.04.2 LTS
> # 
> # locale:
> #  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
> #  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
> #  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
> #  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
> #  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
> # [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
> # 
> # attached base packages:
> # [1] stats     graphics  grDevices utils     datasets  methods   base     
> # 
> # other attached packages:
> #  [1] xtable_1.8-0         texreg_1.36.23       xergm_1.8.2         
> #  [4] GERGM_0.11.2         rem_1.1.2            tnam_1.6.5          
> #  [7] btergm_1.9.0         ggplot2_2.0.0        xergm.common_1.7.7  
> # [10] statnet_2016.4       sna_2.4              ergm.count_3.2.0    
> # [13] tergm_3.4.0          networkDynamic_0.9.0 ergm_3.7.1          
> # [16] network_1.13.0       statnet.common_3.3.0
> # 
> # loaded via a namespace (and not attached):
> #  [1] deSolve_1.12        gtools_3.5.0        lpSolve_5.6.13     
> #  [4] splines_3.3.0       lattice_0.20-33     mstate_0.2.8       
> #  [7] colorspace_1.2-6    flexsurv_0.7        stats4_3.3.0       
> # [10] mgcv_1.8-12         survival_2.39-4     nloptr_1.0.4       
> # [13] RColorBrewer_1.1-2  muhaz_1.2.6         speedglm_0.3-1     
> # [16] trust_0.1-7         plyr_1.8.3          robustbase_0.92-5  
> # [19] munsell_0.4.2       gtable_0.1.2        caTools_1.17.1     
> # [22] mvtnorm_1.0-5       coda_0.18-1         permute_0.8-4      
> # [25] parallel_3.3.0      DEoptimR_1.0-4      Rcpp_0.12.11       
> # [28] KernSmooth_2.23-15  ROCR_1.0-7          scales_0.4.1       
> # [31] gdata_2.17.0        vegan_2.3-1         RcppParallel_4.3.20
> # [34] lme4_1.1-10         gplots_2.17.0       grid_3.3.0         
> # [37] quadprog_1.5-5      tools_3.3.0         bitops_1.0-6       
> # [40] magrittr_1.5        RSiena_1.1-232      cluster_2.0.4      
> # [43] MASS_7.3-45         Matrix_1.2-6        minqa_1.2.4        
> # [46] boot_1.3-17         igraph_1.0.1        nlme_3.1-128       
> 
> ncpus <- 3
> parallel <- "snow"
> set.seed(12345)
> 
> ################################################################################
> # Data preparation: nominations
> ################################################################################
> 
> nominations <- read.csv2("nominations.csv", stringsAsFactors = FALSE)
> names <- read.csv2("names.csv", stringsAsFactors = FALSE)
> chapters <- read.csv2("chapters.csv", stringsAsFactors = FALSE)
> 
> # extract names who answered the survey + including external nominations
> survey.names <- names$name[names$survey.name == 1]
> survey.names.extended <- unique(c(nominations$sender, nominations$receiver, 
+     names$name[names$survey.name == 1]))
> 
> unique(chapters[chapters$role %in% c("lead author", 
+     "coordinating lead author"), ]$person)  # survey population = 493
  [1] "DeFries, Ruth"                             
  [2] "Pagiola, Stephano"                         
  [3] "Adamowicz, W. L."                          
  [4] "Akçakaya, Resit"                           
  [5] "Arcenas, Agustin"                          
  [6] "Babu, Suresh"                              
  [7] "Balk, Deborah"                             
  [8] "Confalonieri, Ulisses"                     
  [9] "Crépin, Anne-Sophie"                       
 [10] "Falconí, Fander"                           
 [11] "Fritz, Steffen"                            
 [12] "Green, Rhys"                               
 [13] "Gutierrez-Espeleta, Edgar E."              
 [14] "Hamilton, Kirk"                            
 [15] "Kane, Racine"                              
 [16] "Matthews, Emily"                           
 [17] "Ricketts, Taylor"                          
 [18] "Yue, Tian Xiang"                           
 [19] "Nelson, Gerald Charles"                    
 [20] "Mace, Georgina"                            
 [21] "Masundire, Hillary M."                     
 [22] "Baillie, Jonathan"                         
 [23] "Brooks, Thomas"                            
 [24] "Hoffmann, Michael"                         
 [25] "Stuart, Simon"                             
 [26] "Balmford, Andrew"                          
 [27] "Purvis, Andy"                              
 [28] "Reyers, Belinda"                           
 [29] "Wang, Jinliang"                            
 [30] "Revenga, Carmen"                           
 [31] "Kennedy, Elizabeth T."                     
 [32] "Naeem, Shahid"                             
 [33] "Alkemade, Rob"                             
 [34] "Allnutt, Tom"                              
 [35] "Bakarr, Mohamed"                           
 [36] "Bond, William"                             
 [37] "Chanson, Janice"                           
 [38] "Cox, Neil"                                 
 [39] "Hilton-Taylor, Craig"                      
 [40] "Loucks, Colby"                             
 [41] "Rodrigues, Ana"                            
 [42] "Sechrest, Wes"                             
 [43] "Stattersfield, Alison"                     
 [44] "van Rensburg, Berndt Janse"                
 [45] "Whiteman, Christina"                       
 [46] "Levy, Marc"                                
 [47] "Rhoe, Valerie"                             
 [48] "Catenazzi, Alessandro"                     
 [49] "Chen, Ma"                                  
 [50] "Reid, Walter V."                           
 [51] "Sengupta, Debdatta"                        
 [52] "Kasperson, Roger E."                       
 [53] "Dow, Kirstin"                              
 [54] "Archer, Emma R. M."                        
 [55] "Cáceres, Daniel"                           
 [56] "Downing, Thomas E."                        
 [57] "Elmqvist, Thomas"                          
 [58] "Eriksen, Siri"                             
 [59] "Folke, Carl"                               
 [60] "Han, Guoyi"                                
 [61] "Iyengar, Kavita"                           
 [62] "Vogel, Coleen Heather"                     
 [63] "Wilson, Kerrie Ann"                        
 [64] "Ziervogel, Gina"                           
 [65] "Vörösmarty, Charles"                       
 [66] "Lévêque, Christian"                        
 [67] "Bos, Robert"                               
 [68] "Caudill, Chirs"                            
 [69] "Chilton, John"                             
 [70] "Douglas, Ellen M."                         
 [71] "Meybeck, Michel"                           
 [72] "Prager, Daniel"                            
 [73] "Wood, Stanley"                             
 [74] "Ehui, Simeon"                              
 [75] "Alder, Jacqueline"                         
 [76] "Benin, Sam"                                
 [77] "Cassman, Kenneth G."                       
 [78] "Cooper, H. David"                          
 [79] "Johns, Timothy"                            
 [80] "Gaskell, Joanne"                           
 [81] "Grainger, Richard"                         
 [82] "Kadungure, Sandra"                         
 [83] "Otte, Joachim"                             
 [84] "Rola, Agnes"                               
 [85] "Watson, Reg"                               
 [86] "Wijkstrom, Ulf"                            
 [87] "Devendra, Canagasaby"                      
 [88] "Sampson, Robert Neil"                      
 [89] "Bystriakova, Nadia"                        
 [90] "Brown, Sandra"                             
 [91] "Gonzalez, Patrick"                         
 [92] "Irland, Lloyd C."                          
 [93] "Kauppi, Pekka"                             
 [94] "Sedjo, Roger"                              
 [95] "Thompson, Ian D."                          
 [96] "Beattie, Andrew"                           
 [97] "Barthlott, Wilhelm"                        
 [98] "Elisabetsky, Elaine"                       
 [99] "Farrell, Roberta Lee"                      
[100] "Kheng, Chua Teck"                          
[101] "Prance, Iain"                              
[102] "Diaz, Sandra"                              
[103] "Tilman, David"                             
[104] "Fargione, Joseph Edward"                   
[105] "Chapin III, F. Stuart"                     
[106] "Dirzo, Rodolfo"                            
[107] "Kitzberger, Thomas"                        
[108] "Gemmill, Barbra"                           
[109] "Zobel, Martin"                             
[110] "Vilà, Monsterrat"                          
[111] "Mitchell, Charles"                         
[112] "Wilby, Andrew"                             
[113] "Daily, Gretchen"                           
[114] "Galetti, Mauro"                            
[115] "Laurance, William"                         
[116] "Pretty, Jules"                             
[117] "Naylor, Rosamond"                          
[118] "Power, Alison"                             
[119] "Harvell, Drew"                             
[120] "Lavelle, Patrick"                          
[121] "Dugdale, Richard"                          
[122] "Scholes, Robert J."                        
[123] "Berhe, Asmereth Asefaw"                    
[124] "Carpenter, Edward"                         
[125] "Codispoti, Lou"                            
[126] "Izac, Anne-Marie"                          
[127] "Lemoalle, Jacques"                         
[128] "Luizao, Flavio"                            
[129] "Scholes, Mary"                             
[130] "Tréguer, Paul"                             
[131] "Ward, Bess"                                
[132] "House, Joanna"                             
[133] "Brovkin, Victor"                           
[134] "Betts, Richard"                            
[135] "Costanza, Robert"                          
[136] "Dias, Maria A. S."                         
[137] "Holland, Beth"                             
[138] "Le Quéré, Corinne"                         
[139] "Kim Phat, Nophea"                          
[140] "Riebesell, Ulf"                            
[141] "Patz, Jonathan"                            
[142] "Chua, Kaw Bing"                            
[143] "Daszak, Peter"                             
[144] "Hyatt, Alex D."                            
[145] "Molyneux, David"                           
[146] "Thomson, Madeleine"                        
[147] "Yameogo, Laurent"                          
[148] "Vasconcelos, Pedro"                        
[149] "Rubio-Palis, Yasmin"                       
[150] "Hinga, Kenneth R."                         
[151] "Batchelor, Allan"                          
[152] "Ahmed, Mohamed Tawfic"                     
[153] "Osibanjo, Oladele"                         
[154] "Bravo de Guenni, Lelys"                    
[155] "Goldammer, Johan"                          
[156] "Hurtt, George"                             
[157] "Mata, Luis Jose"                           
[158] "de Groot, Dolf"                            
[159] "Ramakrishnan, P. S."                       
[160] "van de Berg, Agnes"                        
[161] "Kulenthran, Thayanithi"                    
[162] "Muller, Scott Augustín"                    
[163] "Pitt, David"                               
[164] "Wascher, Dirk"                             
[165] "Wijesuriya, Gamini Senaratne"              
[166] "Pauly, Daniel"                             
[167] "Bakun, Andy"                               
[168] "Heileman, Sherry"                          
[169] "Kock, Karl-Hermann"                        
[170] "Mace, Pamela"                              
[171] "Perrin, William F."                        
[172] "Stergiou, Kostas"                          
[173] "Sumaila, Ussif Rashid"                     
[174] "Vierros, Marjo"                            
[175] "Freitas, Helena"                           
[176] "Sadovy de Mitcheson, Yvonne"               
[177] "Agardy, Tundi"                             
[178] "Dayton, Paul"                              
[179] "Curran, Sara"                              
[180] "Kitchingman, Adrian"                       
[181] "Wilson, Matthew A."                        
[182] "Restrepo, Juan"                            
[183] "Birkeland, Charles Evans"                  
[184] "Blaber, Steve"                             
[185] "Saifullah, Syed"                           
[186] "Branch, George M."                         
[187] "Boersma, P. Dee"                           
[188] "Nixon, Scott"                              
[189] "Dugan, Patrick"                            
[190] "Davidson, Nicholas"                        
[191] "Finlayson, Max"                            
[192] "D'Cruz, Rebecca"                           
[193] "Aladin, Nickolay"                          
[194] "Barker, David Read"                        
[195] "Beltram, Gordana"                          
[196] "Brouwer, Joost"                            
[197] "Duker, Laurie"                             
[198] "Junk, Wolfang Johannes"                    
[199] "Kaplowitz, Michael"                        
[200] "Ketelaars, Henk"                           
[201] "Kreuzberg-Mukhina, Elena"                  
[202] "de la Lanza Espino, Guadalupe"             
[203] "Lopez, Alvin"                              
[204] "Milton, Randy"                             
[205] "Mirabzadeh, Parastu"                       
[206] "Pritchard, Dave"                           
[207] "Rivera, Maria"                             
[208] "Hussainy, Abid Shah"                       
[209] "Silvius, Marcel"                           
[210] "Steinkamp, Melanie J."                     
[211] "Shvidenko, Anatoly"                        
[212] "Barber, Charles V."                        
[213] "Persson, Reidar"                           
[214] "Hassan, Rashid"                            
[215] "Lakyda, Petro"                             
[216] "McCallum, Ian"                             
[217] "Nilsson, Christer"                         
[218] "Pulhin, Juan"                              
[219] "van Rosenburg, Bernardt"                   
[220] "Safriel, Uriel N."                         
[221] "Adeel, Zafar"                              
[222] "Niemeijer, David"                          
[223] "Puidefabregas, Juan"                       
[224] "White, Robin P."                           
[225] "Lal, Rattan"                               
[226] "Winslow, Mark"                             
[227] "Ziedler, Juliane"                          
[228] "Prince, Steve"                             
[229] "King, Caroline"                            
[230] "Wong, Poh Poh"                             
[231] "Marone, Eduardo"                           
[232] "Lana, Paulo"                               
[233] "Fortes, Miguel"                            
[234] "Moro, Dorian"                              
[235] "Agard, John"                               
[236] "Vicente, Luis"                             
[237] "Körner, Christian"                         
[238] "Ohsawa, Masahiko"                          
[239] "Spehn, Eva"                                
[240] "Berge, Erling"                             
[241] "Bugmann, Harald"                           
[242] "Groombridge, Brian"                        
[243] "Hamilton, Lawrence"                        
[244] "Hofer, Thomas"                             
[245] "Ives, Jack"                                
[246] "Jodha, Narpat Singh"                       
[247] "Messerli, Bruno"                           
[248] "Pratt, D. Jane"                            
[249] "Price, Martin Francis"                     
[250] "Reasoner, Mel A."                          
[251] "Rodgers, Alan"                             
[252] "Thönell, Jillian"                          
[253] "Yoshino, Masatoshi"                        
[254] "Berman, Matthew"                           
[255] "Callaghan, Terry"                          
[256] "Convey, Peter"                             
[257] "Danell, Kjell"                             
[258] "Ducklow, Hugh"                             
[259] "Forbes, Bruce"                             
[260] "Kofinas, Gary Peter"                       
[261] "McGuire, David"                            
[262] "Nuttall, Mark"                             
[263] "Virginia, Ross"                            
[264] "Young, Oran"                               
[265] "Zimov, Sergei"                             
[266] "Choo, Poh Sze"                             
[267] "Dixon, John"                               
[268] "Khan, Shahbaz"                             
[269] "Lipper, Leslie"                            
[270] "Primavera, Jurgenna"                       
[271] "Ramankutty, Navin"                         
[272] "Viglizzo, Ernesto Francisco"               
[273] "Wiebe, Keith"                              
[274] "McGranahan, Gordon"                        
[275] "Marcotullio, Peter J."                     
[276] "Bai, Xuemei"                               
[277] "Braga, Tania"                              
[278] "Douglas, Ian"                              
[279] "Rees, William Ernest"                      
[280] "Satterthwaite, David Edward"               
[281] "Songsore, Jacob"                           
[282] "Zlotnik, Hania"                            
[283] "Janetos, Anthony C."                       
[284] "Ash, Neville John"                         
[285] "Latham, John"                              
[286] "Fonseca, Gustavo"                          
[287] "Ximing, Cai"                               
[288] "Amerasinghe, Felix P."                     
[289] "Cardoso, Manoel"                           
[290] "Raskin, Paul"                              
[291] "Monks, Fran"                               
[292] "Ribeiro, Maria Teresa Monteiro Grilo Gomes"
[293] "van Vuuren, Detlef"                        
[294] "Zurek, Monika"                             
[295] "Cumming, Graeme S."                        
[296] "Peterson, Garry"                           
[297] "Kareiva, Peter"                            
[298] "Bennett,Elena"                             
[299] "Butler, Colin David"                       
[300] "Carpenter, Steve"                          
[301] "Cheung, W. W. L."                          
[302] "Vries, Bert de"                            
[303] "Dickinson, Robert E."                      
[304] "Dobson, Andrew"                            
[305] "Foley, Jon"                                
[306] "Geoghegan, Jackie"                         
[307] "Kabat, Pavel"                              
[308] "Keymer, Juan"                              
[309] "Kleidon, Axel"                             
[310] "Lodge, David"                              
[311] "Manson, Steve"                             
[312] "McGlade, Jacquie"                          
[313] "Mooney, Hal"                               
[314] "Parma, Ana"                                
[315] "Pascual, Miguel A."                        
[316] "Pereira, Henrique Miguel"                  
[317] "Rosegrant, Mark"                           
[318] "Ringler, Claudia"                          
[319] "Sala, Osvaldo"                             
[320] "Turner II, Billie Lee"                     
[321] "Wall, Diana"                               
[322] "Wilkinson, Paul"                           
[323] "Wolters, Volkmar"                          
[324] "Cork, Steven John"                         
[325] "Petschel-Held, Gerhardt"                   
[326] "Alcamo, Joseph"                            
[327] "Masui, Toshihiko"                          
[328] "Morita, Tsuneyuki"                         
[329] "Schulze, Kerstin"                          
[330] "Dietz, Thomas"                             
[331] "Dobermann, Achim"                          
[332] "Marco, Diana Elizabeth"                    
[333] "Nakicenovic, Nebojsa"                      
[334] "O'Neill, Brian"                            
[335] "Norgaard, Richard"                         
[336] "Ojima, Denis"                              
[337] "Pingali, Prabhu"                           
[338] "Watson, Robert Toney"                      
[339] "Carr, Edward R."                           
[340] "Deane, Danielle"                           
[341] "Cramer, Wolfgang"                          
[342] "Christensen, Villy"                        
[343] "Maerker, Michael"                          
[344] "Xenopoulos, Marguerite A."                 
[345] "Olouoch-Kosura, Willis"                    
[346] "Corvalan, Carlos"                          
[347] "Fobil, Julius Najah"                       
[348] "Koren, Hillel"                             
[349] "Tancredi, Elda"                            
[350] "Rodriguez, Jon Paul"                       
[351] "Beard, Doug"                               
[352] "Mutale, Michael"                           
[353] "Ma, Shiming"                               
[354] "Robinson, John"                            
[355] "Toth, Ferenc"                              
[356] "Hizsnyik, Eva"                             
[357] "Park, Jacob"                               
[358] "Saterson, Kathryn"                         
[359] "Stott, Andrew"                             
[360] "Chambers, W. Bradnee"                      
[361] "de Soysa, Indra"                           
[362] "Green, Jessica F."                         
[363] "Hirakuri, Sofia"                           
[364] "Isozaki, Hiroji"                           
[365] "Kambu, Alphonse"                           
[366] "Fisher, Dana R."                           
[367] "Simpson, David"                            
[368] "Vira, Bhaskar"                             
[369] "Davidson, Debra J."                        
[370] "Yohe, Gary W."                             
[371] "Adger, Neil"                               
[372] "Dowlatabadi, Hadi"                         
[373] "Ebi, Kristie L."                           
[374] "Huq, Saleemul"                             
[375] "Moran, Dominic"                            
[376] "Rothman, Dale Scott"                       
[377] "Strzepek, Kenneth"                         
[378] "McNeely, Jeffrey A."                       
[379] "Faith, Daniel P."                          
[380] "Albers, Heidi J."                          
[381] "Dulloo, Ehsan"                             
[382] "Goldstein, Wendy"                          
[383] "Polasky, Steve"                            
[384] "Redford, Kent H."                          
[385] "Robinson, Elizabeth J. Z."                 
[386] "Schutyser, Frederik Lieven Jan"            
[387] "Shah, Mahendra"                            
[388] "Xepapadeas, Anastasios"                    
[389] "Entsua-Mensah, Rose Emma Mamaa"            
[390] "Fisher, Günther"                           
[391] "Haslberger, Alexander"                     
[392] "Jensen, Frank"                             
[393] "Mirza, Monirul"                            
[394] "Sartzetakis, Eftichios"                    
[395] "Simons, Henk"                              
[396] "Aylward, Bruce"                            
[397] "Bandyopadhyay, Jayanta"                    
[398] "Belausteguigotia, Juan-Carlos"             
[399] "Börkey, Peter"                             
[400] "Cassar, Angela"                            
[401] "Meadows, Laura"                            
[402] "Saade, Lilian"                             
[403] "Siebentritt, Mark Anthony"                 
[404] "Stein, Robyn"                              
[405] "Tognetti, Sylvia S."                       
[406] "Tortajada, Cecilia"                        
[407] "Sizer, Nigel"                              
[408] "Bass, Steve"                               
[409] "Mayers, James"                             
[410] "Arnold, Mike"                              
[411] "Auckland, Louise"                          
[412] "Belcher, Brian"                            
[413] "Bird, Neil"                                
[414] "Campbell, Bruce"                           
[415] "Carle, Jim"                                
[416] "Cleary, David"                             
[417] "Counsell, Simon"                           
[418] "Enters, Thomas"                            
[419] "Fernando, Petra Karin"                     
[420] "Gullison, Ted"                             
[421] "Hudson, John"                              
[422] "Kellison, Bob"                             
[423] "Klingberg, Tage"                           
[424] "Owen, Carlton N."                          
[425] "Vermeulen, Sonja Joy"                      
[426] "Wollenberg, Eva"                           
[427] "Shackleton, Sheona"                        
[428] "Edmunds, David"                            
[429] "Howarth, Robert"                           
[430] "Ramakrishna, Kilaparti"                    
[431] "Choi, Euiso"                               
[432] "Elmgren, Ragnar"                           
[433] "Martinelli, Luiz"                          
[434] "Mendoza-Escalante, Arisbe"                 
[435] "Moomaw, William"                           
[436] "Palm, Cheryl Ann"                          
[437] "Roy, Rabindra"                             
[438] "Zhao-Liang, Zhu"                           
[439] "Adedipe, Nimbe O."                         
[440] "Sridhar, M. K. C."                         
[441] "Baker, Joseph Thomas"                      
[442] "Patwardhan, Anand"                         
[443] "Attzs, Marlene"                            
[444] "Marchand, Marcel"                          
[445] "Campbell-Lendrum, Diamid"                  
[446] "Davies, Clive"                             
[447] "Fletcher, Elaine"                          
[448] "Schofield, Christopher"                    
[449] "Hougard, Jean-Marc"                        
[450] "Polson, Karen Alicia"                      
[451] "Sinkins, Steven"                           
[452] "Noble, Ian"                                
[453] "Parikh, Jyoti"                             
[454] "Howarth, Richard B."                       
[455] "Klein, Richard J. T."                      
[456] "Bhattacharya, D. K."                       
[457] "Brondizio, Eduardo S."                     
[458] "Spierenburg, Marja"                        
[459] "Ghosh, Abhik"                              
[460] "Traverse, Myrle"                           
[461] "Brown, Katrina"                            
[462] "Mackensen, Jens"                           
[463] "Rosendo, Sergio"                           
[464] "Viswanathan, K. Kuperan"                   
[465] "Cimarrusti, Lina"                          
[466] "Morsello, Carla"                           
[467] "Muchagata, Marcia"                         
[468] "Siason, Ida"                               
[469] "Singh, Shekhar"                            
[470] "Susilowati, Indah"                         
[471] "Hales, Simon"                              
[472] "Woodward, Alistair"                        
[473] "de Avila Pires, Fernando Dias"             
[474] "Soskolne, Colin L."                        
[475] "Duraiappah, Anantha Kumar"                 
[476] "Comim, Flavio"                             
[477] "de Oliveira, Thierry"                      
[478] "Gupta, Joyeeta"                            
[479] "Kumar, Pushpam"                            
[480] "Pyoos, Marjorie"                           
[481] "Moldan, Bedrich"                           
[482] "Riley, Janet"                              
[483] "Hak, Tomas"                                
[484] "Rivera, Jorge"                             
[485] "Rabbinge, Rudy"                            
[486] "Gallopín, Gilberto Carlos"                 
[487] "Khoday, Kishan"                            
[488] "Lewis, Nancy"                              
[489] "Lubchenco, Jane"                           
[490] "Melillo, Jerry"                            
[491] "Schmidt-Traub, Guido"                      
[492] "Sombilla, Mercedita A."                    
[493] "Wellesley Percy, Steven"                   
> 
> # create matrix and network object of nomination patterns within survey
> nomin.short <- nominations[nominations$sender %in% survey.names & 
+     nominations$receiver %in% survey.names, ]
> nomin.mat <- matrix(0, nrow = length(survey.names), ncol = length(survey.names))
> for (i in 1:nrow(nomin.short)) {
+   send <- which(survey.names == nomin.short$sender[i])
+   rec <- which(survey.names == nomin.short$receiver[i])
+   nomin.mat[send, rec] <- nomin.mat[send, rec] + 1
+ }
> rownames(nomin.mat) <- colnames(nomin.mat) <- survey.names
> 
> # create network of nomination patterns including external nominations
> nomin.mat.extended <- matrix(0, nrow = length(survey.names.extended), ncol = 
+     length(survey.names.extended))
> for (i in 1:nrow(nominations)) {
+   send <- which(survey.names.extended == nominations$sender[i])
+   rec <- which(survey.names.extended == nominations$receiver[i])
+   nomin.mat.extended[send, rec] <- nomin.mat.extended[send, rec] + 1
+ }
> rownames(nomin.mat.extended) <- survey.names.extended
> colnames(nomin.mat.extended) <- survey.names.extended
> extended.in.short <- survey.names.extended %in% survey.names
> 
> 
> ################################################################################
> # Data preparation: institutions
> ################################################################################
> 
> institutions <- read.csv2("institutions.csv", stringsAsFactors = FALSE)
> inst.unique <- unique(institutions$institution)
> inst.mat <- matrix(0, nrow = nrow(nomin.mat), ncol = length(inst.unique))
> rownames(inst.mat) <- survey.names
> colnames(inst.mat) <- inst.unique
> 
> for (i in 1:nrow(institutions)) {
+   row.index <- which(survey.names == institutions$name[i])
+   col.index <- which(inst.unique == institutions$institution[i])
+   inst.mat[row.index, col.index] <- 1
+ }
> 
> inst.num <- rowSums(inst.mat)  # number of institutional memberships
> inst.cooc <- inst.mat %*% t(inst.mat)  # co-occurrence matrix
> inst.cooc.sq <- inst.cooc^2  # squared co-occurrence matrix
> 
> 
> ################################################################################
> # Data preparation: chapter collaboration
> ################################################################################
> 
> # create networks of chapters (only survey respondents)
> chapters.short <- chapters[chapters$person %in% survey.names, ]
> chapterlist <- unique(chapters$chapter)
> chapters.mat.bip <- matrix(0, nrow = length(survey.names), ncol = 
+     length(chapterlist))
> for (i in 1:nrow(chapters.short)) {
+   pers <- which(survey.names == chapters.short$person[i])
+   chap <- which(chapterlist == chapters.short$chapter[i])
+   chapters.mat.bip[pers, chap] <- chapters.mat.bip[pers, chap] + 1
+ }
> chapters.mat.bip <- chapters.mat.bip[, colSums(chapters.mat.bip) > 0]
> chapters.mat <- chapters.mat.bip %*% t(chapters.mat.bip)
> chapters.mat.squared <- chapters.mat^2  # for ERGM edgecov
> chapters.mat.binary <- 1 * (chapters.mat > 0)  # for ERGM edgecov
> rownames(chapters.mat.binary) <- survey.names
> colnames(chapters.mat.binary) <- survey.names
>   
> # create networks of chapters (extended to external nominations)
> chapters.extended <- chapters[chapters$person %in% survey.names.extended, ]
> chapterlist.extended <- unique(chapters.extended$chapter)
> chapters.mat.bip.extended <- matrix(0, nrow = length(survey.names.extended), 
+     ncol = length(chapterlist.extended))
> for (i in 1:nrow(chapters.extended)) {
+   pers <- which(survey.names.extended == chapters.extended$person[i])
+   chap <- which(chapterlist.extended == chapters.extended$chapter[i])
+   chapters.mat.bip.extended[pers, chap] <- chapters.mat.bip.extended[pers, 
+       chap] + 1
+ }
> chapters.mat.extended <- chapters.mat.bip.extended %*% 
+     t(chapters.mat.bip.extended)
> 
> # create networks of chapters (all persons)
> chapterlist.all <- unique(chapters$chapter)
> persons.all <- unique(chapters$person)
> chapters.mat.bip.all <- matrix(0, nrow = length(persons.all), ncol = 
+     length(chapterlist.all))
> for (i in 1:nrow(chapters)) {
+   pers <- which(persons.all == chapters$person[i])
+   chap <- which(chapterlist.all == chapters$chapter[i])
+   chapters.mat.bip.all[pers, chap] <- chapters.mat.bip.all[pers, chap] + 1
+ }
> chapters.mat.all <- chapters.mat.bip.all %*% t(chapters.mat.bip.all)
> 
> 
> ################################################################################
> # Data preparation: covariates/attributes
> ################################################################################
> 
> # create covariates from names table (only survey respondents)
> names.short <- names[names$name %in% survey.names, ]
> socsci <- character(length(survey.names))
> gender <- socsci
> nationality <- socsci
> school <- socsci
> expertise <- socsci
> education <- socsci
> country <- socsci
> employer <- socsci
> authortype <- socsci
> leader <- numeric(length(survey.names))
> for (i in 1:nrow(names.short)) {
+   index <- which(survey.names == names.short$name[i])
+   if (names.short$socsci[i] == "") {
+     socsci[index] <- NA
+   } else {
+     socsci[index] <- names.short$socsci[i]
+   }
+   if (names.short$gender[i] == "") {
+     gender[index] <- NA
+   } else {
+     gender[index] <- names.short$gender[i]
+   }
+   if (names.short$nationality[i] == "") {
+     nationality[index] <- NA
+   } else {
+     nationality[index] <- names.short$nationality[i]
+   }
+   if (names.short$school[i] == "") {
+     school[index] <- NA
+   } else {
+     school[index] <- names.short$school[i]
+   }
+   if (names.short$expertise[i] == "") {
+     expertise[index] <- NA
+   } else {
+     expertise[index] <- names.short$expertise[i]
+   }
+   if (names.short$education[i] == "") {
+     education[index] <- NA
+   } else {
+     education[index] <- names.short$education[i]
+   }
+   if (names.short$country[i] == "") {
+     country[index] <- NA
+   } else {
+     country[index] <- names.short$country[i]
+   }
+   if (names.short$employer[i] == "") {
+     employer[index] <- NA
+   } else {
+     employer[index] <- names.short$employer[i]
+   }
+   if (names.short$director[index] == "yes" || 
+       names.short$assessment_panel[index] == "yes" || 
+       names.short$wg_editor[index] == "yes") {
+     leader[index] <- 1
+   } else {
+     leader[index] <- 0
+   }
+ }
> 
> chap.indices <- match(names.short$name, chapters$person)
> for (i in 1:length(names.short$name)) {
+   authortype[i] <- chapters$role[chap.indices[i]]
+ }
> authortype[is.na(authortype)] <- "NA"
> 
> attributes.short <- data.frame(name = survey.names, socsci = socsci, gender = 
+     gender, nationality = nationality, school = school, expertise = expertise, 
+     education = education, country = country, employer = employer, 
+     authortype = authortype, stringsAsFactors = FALSE)
> 
> # create covariates from names table (extended names from survey)
> names.extended <- names[names$name %in% survey.names.extended, ]
> socsci <- character(length(survey.names.extended))
> gender <- socsci
> nationality <- socsci
> school <- socsci
> expertise <- socsci
> education <- socsci
> country <- socsci
> employer <- socsci
> director <- logical(length(survey.names.extended))
> wg.editor <- director
> asspanel <- director
> cla <- director
> la <- director
> for (i in 1:nrow(names.extended)) {
+   index <- which(survey.names.extended == names.extended$name[i])
+   if (names.extended$socsci[i] == "") {
+     socsci[index] <- NA
+   } else {
+     socsci[index] <- names.extended$socsci[i]
+   }
+   if (names.extended$gender[i] == "") {
+     gender[index] <- NA
+   } else {
+     gender[index] <- names.extended$gender[i]
+   }
+   if (names.extended$nationality[i] == "") {
+     nationality[index] <- NA
+   } else {
+     nationality[index] <- names.extended$nationality[i]
+   }
+   if (names.extended$school[i] == "") {
+     school[index] <- NA
+   } else {
+     school[index] <- names.extended$school[i]
+   }
+   if (names.extended$expertise[i] == "") {
+     expertise[index] <- NA
+   } else {
+     expertise[index] <- names.extended$expertise[i]
+   }
+   if (names.extended$education[i] == "") {
+     education[index] <- NA
+   } else {
+     education[index] <- names.extended$education[i]
+   }
+   if (names.extended$country[i] == "") {
+     country[index] <- NA
+   } else {
+     country[index] <- names.extended$country[i]
+   }
+   if (names.extended$employer[i] == "") {
+     employer[index] <- NA
+   } else {
+     employer[index] <- names.extended$employer[i]
+   }
+   if (names.extended$director[i] == "yes") {
+     director[index] <- TRUE
+   } else {
+     director[index] <- FALSE
+   }
+   if (names.extended$assessment_panel[i] == "yes") {
+     asspanel[index] <- TRUE
+   } else {
+     asspanel[index] <- FALSE
+   }
+   if (names.extended$wg_editor[i] == "yes") {
+     wg.editor[index] <- TRUE
+   } else {
+     wg.editor[index] <- FALSE
+   }
+ }
> 
> attributes.extended <- data.frame(name = survey.names.extended, 
+     socsci = socsci, gender = gender, nationality = nationality, 
+     school = school, expertise = expertise, education = education, 
+     country = country, employer = employer, stringsAsFactors = FALSE)
> 
> # compile CLA and LA roles for cross-table
> chap.indices <- match(survey.names.extended, chapters$person)
> for (i in 1:length(survey.names.extended)) {
+   if (!is.na(chap.indices[i]) && 
+       chapters$role[chap.indices[i]] == "coordinating lead author") {
+     cla[i] <- TRUE
+   } else {
+     cla[i] <- FALSE
+   }
+   if (!is.na(chap.indices[i]) && 
+       chapters$role[chap.indices[i]] == "lead author") {
+     la[i] <- TRUE
+   } else {
+     la[i] <- FALSE
+   }
+ }
> 
> 
> ################################################################################
> # Data preparation: create network objects and covariates for ERGM
> ################################################################################
> 
> # create network objects with nodal attributes
> nomin.short.nw <- network(nomin.mat, directed = TRUE, bipartite = FALSE)
> set.vertex.attribute(nomin.short.nw, "socsci", attributes.short$socsci)
> set.vertex.attribute(nomin.short.nw, "gender", attributes.short$gender)
> set.vertex.attribute(nomin.short.nw, "nationality", 
+     attributes.short$nationality)
> set.vertex.attribute(nomin.short.nw, "school", attributes.short$school)
> set.vertex.attribute(nomin.short.nw, "expertise", attributes.short$expertise)
> set.vertex.attribute(nomin.short.nw, "education", attributes.short$education)
> set.vertex.attribute(nomin.short.nw, "country", attributes.short$country)
> set.vertex.attribute(nomin.short.nw, "employer", attributes.short$employer)
> set.vertex.attribute(nomin.short.nw, "inst.num", inst.num)
> set.vertex.attribute(nomin.short.nw, "authortype", authortype)
> set.vertex.attribute(nomin.short.nw, "leader", leader)
> 
> # create network object with edge attribute for extended matrix
> nomin.extended.nw <- network(nomin.mat.extended, directed = TRUE, 
+     bipartite = FALSE)
> set.vertex.attribute(nomin.extended.nw, "socsci", attributes.extended$socsci)
> set.vertex.attribute(nomin.extended.nw, "gender", attributes.extended$gender)
> set.vertex.attribute(nomin.extended.nw, "nationality", 
+     attributes.extended$nationality)
> set.vertex.attribute(nomin.extended.nw, "school", attributes.extended$school)
> set.vertex.attribute(nomin.extended.nw, "expertise", 
+     attributes.extended$expertise)
> set.vertex.attribute(nomin.extended.nw, "education", 
+     attributes.extended$education)
> set.vertex.attribute(nomin.extended.nw, "country", attributes.extended$country)
> set.vertex.attribute(nomin.extended.nw, "employer", 
+     attributes.extended$employer)
> diag(nomin.mat.extended) <- 0
> edgeattrib.mat <- matrix(0, nrow = nrow(nomin.mat.extended), 
+     ncol = ncol(nomin.mat.extended))
> edgeattrib.vector <- numeric(0)
> for (i in 1:nrow(nomin.mat.extended)) {
+   for (j in 1:ncol(nomin.mat.extended)) {
+     if (nomin.mat.extended[i, j] > 0) {
+       if (survey.names.extended[i] %in% survey.names && 
+           survey.names.extended[j] %in% survey.names) {
+         edgeattrib.vector <- c(edgeattrib.vector, 1)
+       } else {
+         edgeattrib.vector <- c(edgeattrib.vector, 0)
+       }
+     }
+   }
+ }
> set.edge.attribute(nomin.extended.nw, "respondent", edgeattrib.vector)
> 
> # create country-nationality nodematch covariate matrix
> country.mat <- matrix(0, nrow = nrow(nomin.mat), ncol = ncol(nomin.mat))
> for (i in 1:nrow(country.mat)) {
+   for (j in 1:ncol(country.mat)) {
+     ci <- attributes.short$country[i]
+     cj <- attributes.short$country[j]
+     ni <- attributes.short$nationality[i]
+     nj <- attributes.short$nationality[j]
+     if (!is.na(ci) && !is.na(cj) && ci == cj) {
+       country.mat[i, j] <- 1
+     }
+     if (!is.na(ni) && !is.na(nj) && ni == nj) {
+       country.mat[i, j] <- 1
+     }
+     if (!is.na(ci) && !is.na(nj) && ci == nj) {
+       country.mat[i, j] <- 1
+     }
+     if (!is.na(ni) && !is.na(cj) && ni == cj) {
+       country.mat[i, j] <- 1
+     }
+   }
+ }
> 
> # recode education into science/PhD versus non-science
> science <- rep(0, nrow(attributes.short))
> for (i in 1:nrow(attributes.short)) {
+   edu <- attributes.short$education[i]
+   if (edu %in% c("Doctorate/PhD", "M.D./PhD")) {
+     science[i] <- 1
+   }
+ }
> nomin.short.nw <- set.vertex.attribute(nomin.short.nw, "phd", science)
> 
> # reachability centrality in the extended network
> reach <- rowSums(reachability(nomin.mat.extended))
Node 1, Reach 12, Total 12
Node 2, Reach 2, Total 14
Node 3, Reach 8, Total 22
Node 4, Reach 6, Total 28
Node 5, Reach 5, Total 33
Node 6, Reach 14, Total 47
Node 7, Reach 5, Total 52
Node 8, Reach 100, Total 152
Node 9, Reach 20, Total 172
Node 10, Reach 6, Total 178
Node 11, Reach 1, Total 179
Node 12, Reach 4, Total 183
Node 13, Reach 2, Total 185
Node 14, Reach 2, Total 187
Node 15, Reach 2, Total 189
Node 16, Reach 2, Total 191
Node 17, Reach 2, Total 193
Node 18, Reach 4, Total 197
Node 19, Reach 2, Total 199
Node 20, Reach 12, Total 211
Node 21, Reach 5, Total 216
Node 22, Reach 4, Total 220
Node 23, Reach 15, Total 235
Node 24, Reach 5, Total 240
Node 25, Reach 19, Total 259
Node 26, Reach 8, Total 267
Node 27, Reach 2, Total 269
Node 28, Reach 7, Total 276
Node 29, Reach 3, Total 279
Node 30, Reach 3, Total 282
Node 31, Reach 3, Total 285
Node 32, Reach 12, Total 297
Node 33, Reach 9, Total 306
Node 34, Reach 13, Total 319
Node 35, Reach 5, Total 324
Node 36, Reach 5, Total 329
Node 37, Reach 2, Total 331
Node 38, Reach 3, Total 334
Node 39, Reach 24, Total 358
Node 40, Reach 10, Total 368
Node 41, Reach 10, Total 378
Node 42, Reach 3, Total 381
Node 43, Reach 3, Total 384
Node 44, Reach 2, Total 386
Node 45, Reach 2, Total 388
Node 46, Reach 2, Total 390
Node 47, Reach 7, Total 397
Node 48, Reach 2, Total 399
Node 49, Reach 2, Total 401
Node 50, Reach 7, Total 408
Node 51, Reach 3, Total 411
Node 52, Reach 12, Total 423
Node 53, Reach 2, Total 425
Node 54, Reach 2, Total 427
Node 55, Reach 12, Total 439
Node 56, Reach 2, Total 441
Node 57, Reach 5, Total 446
Node 58, Reach 16, Total 462
Node 59, Reach 4, Total 466
Node 60, Reach 15, Total 481
Node 61, Reach 4, Total 485
Node 62, Reach 3, Total 488
Node 63, Reach 22, Total 510
Node 64, Reach 2, Total 512
Node 65, Reach 25, Total 537
Node 66, Reach 2, Total 539
Node 67, Reach 7, Total 546
Node 68, Reach 4, Total 550
Node 69, Reach 2, Total 552
Node 70, Reach 2, Total 554
Node 71, Reach 2, Total 556
Node 72, Reach 9, Total 565
Node 73, Reach 2, Total 567
Node 74, Reach 21, Total 588
Node 75, Reach 4, Total 592
Node 76, Reach 2, Total 594
Node 77, Reach 2, Total 596
Node 78, Reach 2, Total 598
Node 79, Reach 2, Total 600
Node 80, Reach 2, Total 602
Node 81, Reach 2, Total 604
Node 82, Reach 2, Total 606
Node 83, Reach 2, Total 608
Node 84, Reach 2, Total 610
Node 85, Reach 4, Total 614
Node 86, Reach 3, Total 617
Node 87, Reach 2, Total 619
Node 88, Reach 6, Total 625
Node 89, Reach 12, Total 637
Node 90, Reach 3, Total 640
Node 91, Reach 2, Total 642
Node 92, Reach 3, Total 645
Node 93, Reach 3, Total 648
Node 94, Reach 2, Total 650
Node 95, Reach 2, Total 652
Node 96, Reach 3, Total 655
Node 97, Reach 8, Total 663
Node 98, Reach 2, Total 665
Node 99, Reach 3, Total 668
Node 100, Reach 2, Total 670
Node 101, Reach 2, Total 672
Node 102, Reach 2, Total 674
Node 103, Reach 2, Total 676
Node 104, Reach 14, Total 690
Node 105, Reach 2, Total 692
Node 106, Reach 9, Total 701
Node 107, Reach 9, Total 710
Node 108, Reach 4, Total 714
Node 109, Reach 2, Total 716
Node 110, Reach 164, Total 880
Node 111, Reach 2, Total 882
Node 112, Reach 6, Total 888
Node 113, Reach 5, Total 893
Node 114, Reach 11, Total 904
Node 115, Reach 2, Total 906
Node 116, Reach 6, Total 912
Node 117, Reach 2, Total 914
Node 118, Reach 5, Total 919
Node 119, Reach 2, Total 921
Node 120, Reach 4, Total 925
Node 121, Reach 3, Total 928
Node 122, Reach 2, Total 930
Node 123, Reach 3, Total 933
Node 124, Reach 7, Total 940
Node 125, Reach 2, Total 942
Node 126, Reach 2, Total 944
Node 127, Reach 3, Total 947
Node 128, Reach 2, Total 949
Node 129, Reach 11, Total 960
Node 130, Reach 2, Total 962
Node 131, Reach 13, Total 975
Node 132, Reach 2, Total 977
Node 133, Reach 10, Total 987
Node 134, Reach 5, Total 992
Node 135, Reach 7, Total 999
Node 136, Reach 2, Total 1001
Node 137, Reach 4, Total 1005
Node 138, Reach 2, Total 1007
Node 139, Reach 4, Total 1011
Node 140, Reach 2, Total 1013
Node 141, Reach 2, Total 1015
Node 142, Reach 7, Total 1022
Node 143, Reach 2, Total 1024
Node 144, Reach 2, Total 1026
Node 145, Reach 2, Total 1028
Node 146, Reach 6, Total 1034
Node 147, Reach 3, Total 1037
Node 148, Reach 12, Total 1049
Node 149, Reach 3, Total 1052
Node 150, Reach 7, Total 1059
Node 151, Reach 2, Total 1061
Node 152, Reach 12, Total 1073
Node 153, Reach 22, Total 1095
Node 154, Reach 9, Total 1104
Node 155, Reach 5, Total 1109
Node 156, Reach 2, Total 1111
Node 157, Reach 2, Total 1113
Node 158, Reach 11, Total 1124
Node 159, Reach 5, Total 1129
Node 160, Reach 12, Total 1141
Node 161, Reach 4, Total 1145
Node 162, Reach 5, Total 1150
Node 163, Reach 4, Total 1154
Node 164, Reach 2, Total 1156
Node 165, Reach 5, Total 1161
Node 166, Reach 2, Total 1163
Node 167, Reach 4, Total 1167
Node 168, Reach 2, Total 1169
Node 169, Reach 2, Total 1171
Node 170, Reach 11, Total 1182
Node 171, Reach 2, Total 1184
Node 172, Reach 5, Total 1189
Node 173, Reach 3, Total 1192
Node 174, Reach 3, Total 1195
Node 175, Reach 2, Total 1197
Node 176, Reach 6, Total 1203
Node 177, Reach 9, Total 1212
Node 178, Reach 8, Total 1220
Node 179, Reach 1, Total 1221
Node 180, Reach 1, Total 1222
Node 181, Reach 1, Total 1223
Node 182, Reach 1, Total 1224
Node 183, Reach 1, Total 1225
Node 184, Reach 1, Total 1226
Node 185, Reach 1, Total 1227
Node 186, Reach 1, Total 1228
Node 187, Reach 1, Total 1229
Node 188, Reach 1, Total 1230
Node 189, Reach 1, Total 1231
Node 190, Reach 1, Total 1232
Node 191, Reach 1, Total 1233
Node 192, Reach 1, Total 1234
Node 193, Reach 1, Total 1235
Node 194, Reach 1, Total 1236
Node 195, Reach 1, Total 1237
Node 196, Reach 1, Total 1238
Node 197, Reach 1, Total 1239
Node 198, Reach 1, Total 1240
Node 199, Reach 1, Total 1241
Node 200, Reach 1, Total 1242
Node 201, Reach 1, Total 1243
Node 202, Reach 1, Total 1244
Node 203, Reach 1, Total 1245
Node 204, Reach 1, Total 1246
Node 205, Reach 1, Total 1247
Node 206, Reach 1, Total 1248
Node 207, Reach 1, Total 1249
Node 208, Reach 1, Total 1250
Node 209, Reach 1, Total 1251
Node 210, Reach 1, Total 1252
Node 211, Reach 1, Total 1253
Node 212, Reach 1, Total 1254
Node 213, Reach 1, Total 1255
Node 214, Reach 1, Total 1256
Node 215, Reach 1, Total 1257
Node 216, Reach 1, Total 1258
Node 217, Reach 1, Total 1259
Node 218, Reach 1, Total 1260
Node 219, Reach 1, Total 1261
Node 220, Reach 1, Total 1262
Node 221, Reach 1, Total 1263
Node 222, Reach 1, Total 1264
Node 223, Reach 1, Total 1265
Node 224, Reach 1, Total 1266
Node 225, Reach 1, Total 1267
Node 226, Reach 1, Total 1268
Node 227, Reach 1, Total 1269
Node 228, Reach 1, Total 1270
Node 229, Reach 1, Total 1271
Node 230, Reach 1, Total 1272
Node 231, Reach 1, Total 1273
Node 232, Reach 1, Total 1274
Node 233, Reach 1, Total 1275
Node 234, Reach 1, Total 1276
Node 235, Reach 1, Total 1277
Node 236, Reach 1, Total 1278
Node 237, Reach 1, Total 1279
Node 238, Reach 1, Total 1280
Node 239, Reach 1, Total 1281
Node 240, Reach 1, Total 1282
Node 241, Reach 1, Total 1283
Node 242, Reach 1, Total 1284
Node 243, Reach 1, Total 1285
Node 244, Reach 1, Total 1286
Node 245, Reach 1, Total 1287
Node 246, Reach 1, Total 1288
Node 247, Reach 1, Total 1289
Node 248, Reach 1, Total 1290
Node 249, Reach 1, Total 1291
Node 250, Reach 1, Total 1292
Node 251, Reach 1, Total 1293
Node 252, Reach 1, Total 1294
Node 253, Reach 1, Total 1295
Node 254, Reach 1, Total 1296
Node 255, Reach 1, Total 1297
Node 256, Reach 1, Total 1298
Node 257, Reach 1, Total 1299
Node 258, Reach 1, Total 1300
Node 259, Reach 1, Total 1301
Node 260, Reach 1, Total 1302
Node 261, Reach 1, Total 1303
Node 262, Reach 1, Total 1304
Node 263, Reach 1, Total 1305
Node 264, Reach 1, Total 1306
Node 265, Reach 1, Total 1307
Node 266, Reach 1, Total 1308
Node 267, Reach 1, Total 1309
Node 268, Reach 1, Total 1310
Node 269, Reach 1, Total 1311
Node 270, Reach 1, Total 1312
Node 271, Reach 1, Total 1313
Node 272, Reach 1, Total 1314
Node 273, Reach 1, Total 1315
Node 274, Reach 1, Total 1316
Node 275, Reach 1, Total 1317
Node 276, Reach 1, Total 1318
Node 277, Reach 1, Total 1319
Node 278, Reach 1, Total 1320
Node 279, Reach 1, Total 1321
Node 280, Reach 1, Total 1322
Node 281, Reach 1, Total 1323
Node 282, Reach 1, Total 1324
Node 283, Reach 1, Total 1325
Node 284, Reach 1, Total 1326
Node 285, Reach 1, Total 1327
Node 286, Reach 1, Total 1328
Node 287, Reach 1, Total 1329
Node 288, Reach 1, Total 1330
Node 289, Reach 1, Total 1331
Node 290, Reach 1, Total 1332
Node 291, Reach 1, Total 1333
Node 292, Reach 1, Total 1334
Node 293, Reach 1, Total 1335
Node 294, Reach 1, Total 1336
Node 295, Reach 1, Total 1337
Node 296, Reach 1, Total 1338
Node 297, Reach 1, Total 1339
Node 298, Reach 1, Total 1340
Node 299, Reach 1, Total 1341
Node 300, Reach 1, Total 1342
Node 301, Reach 1, Total 1343
Node 302, Reach 1, Total 1344
Node 303, Reach 1, Total 1345
Node 304, Reach 1, Total 1346
Node 305, Reach 1, Total 1347
Node 306, Reach 1, Total 1348
Node 307, Reach 1, Total 1349
Node 308, Reach 1, Total 1350
Node 309, Reach 1, Total 1351
Node 310, Reach 1, Total 1352
Node 311, Reach 1, Total 1353
Node 312, Reach 1, Total 1354
Node 313, Reach 1, Total 1355
Node 314, Reach 1, Total 1356
Node 315, Reach 1, Total 1357
Node 316, Reach 1, Total 1358
Node 317, Reach 1, Total 1359
Node 318, Reach 1, Total 1360
Node 319, Reach 1, Total 1361
Node 320, Reach 1, Total 1362
Node 321, Reach 1, Total 1363
Node 322, Reach 1, Total 1364
Node 323, Reach 1, Total 1365
Node 324, Reach 1, Total 1366
Node 325, Reach 1, Total 1367
Node 326, Reach 1, Total 1368
Node 327, Reach 1, Total 1369
Node 328, Reach 1, Total 1370
Node 329, Reach 1, Total 1371
Node 330, Reach 1, Total 1372
Node 331, Reach 1, Total 1373
Node 332, Reach 1, Total 1374
Node 333, Reach 1, Total 1375
Node 334, Reach 1, Total 1376
Node 335, Reach 1, Total 1377
Node 336, Reach 1, Total 1378
Node 337, Reach 1, Total 1379
Node 338, Reach 1, Total 1380
Node 339, Reach 1, Total 1381
Node 340, Reach 1, Total 1382
Node 341, Reach 1, Total 1383
Node 342, Reach 1, Total 1384
Node 343, Reach 1, Total 1385
Node 344, Reach 1, Total 1386
Node 345, Reach 1, Total 1387
Node 346, Reach 1, Total 1388
Node 347, Reach 1, Total 1389
Node 348, Reach 1, Total 1390
Node 349, Reach 1, Total 1391
Node 350, Reach 1, Total 1392
Node 351, Reach 1, Total 1393
Node 352, Reach 1, Total 1394
Node 353, Reach 1, Total 1395
Node 354, Reach 1, Total 1396
Node 355, Reach 1, Total 1397
Node 356, Reach 1, Total 1398
Node 357, Reach 1, Total 1399
Node 358, Reach 1, Total 1400
Node 359, Reach 1, Total 1401
Node 360, Reach 1, Total 1402
Node 361, Reach 1, Total 1403
Node 362, Reach 1, Total 1404
Node 363, Reach 1, Total 1405
Node 364, Reach 1, Total 1406
Node 365, Reach 1, Total 1407
Node 366, Reach 1, Total 1408
Node 367, Reach 1, Total 1409
Node 368, Reach 1, Total 1410
Node 369, Reach 1, Total 1411
Node 370, Reach 1, Total 1412
Node 371, Reach 1, Total 1413
Node 372, Reach 1, Total 1414
Node 373, Reach 1, Total 1415
Node 374, Reach 1, Total 1416
Node 375, Reach 1, Total 1417
Node 376, Reach 1, Total 1418
Node 377, Reach 1, Total 1419
Node 378, Reach 1, Total 1420
Node 379, Reach 1, Total 1421
Node 380, Reach 1, Total 1422
Node 381, Reach 1, Total 1423
Node 382, Reach 1, Total 1424
Node 383, Reach 1, Total 1425
Node 384, Reach 1, Total 1426
Node 385, Reach 1, Total 1427
Node 386, Reach 1, Total 1428
Node 387, Reach 1, Total 1429
Node 388, Reach 1, Total 1430
Node 389, Reach 1, Total 1431
Node 390, Reach 1, Total 1432
Node 391, Reach 1, Total 1433
Node 392, Reach 1, Total 1434
Node 393, Reach 1, Total 1435
Node 394, Reach 1, Total 1436
Node 395, Reach 1, Total 1437
Node 396, Reach 1, Total 1438
Node 397, Reach 1, Total 1439
Node 398, Reach 1, Total 1440
Node 399, Reach 1, Total 1441
Node 400, Reach 1, Total 1442
Node 401, Reach 1, Total 1443
Node 402, Reach 1, Total 1444
Node 403, Reach 1, Total 1445
Node 404, Reach 1, Total 1446
Node 405, Reach 1, Total 1447
Node 406, Reach 1, Total 1448
Node 407, Reach 1, Total 1449
Node 408, Reach 1, Total 1450
Node 409, Reach 1, Total 1451
Node 410, Reach 1, Total 1452
Node 411, Reach 1, Total 1453
Node 412, Reach 1, Total 1454
Node 413, Reach 1, Total 1455
Node 414, Reach 1, Total 1456
Node 415, Reach 1, Total 1457
Node 416, Reach 1, Total 1458
Node 417, Reach 1, Total 1459
Node 418, Reach 1, Total 1460
Node 419, Reach 1, Total 1461
Node 420, Reach 1, Total 1462
Node 421, Reach 1, Total 1463
Node 422, Reach 1, Total 1464
Node 423, Reach 1, Total 1465
Node 424, Reach 1, Total 1466
Node 425, Reach 1, Total 1467
Node 426, Reach 1, Total 1468
Node 427, Reach 1, Total 1469
Node 428, Reach 1, Total 1470
Node 429, Reach 1, Total 1471
Node 430, Reach 1, Total 1472
Node 431, Reach 1, Total 1473
Node 432, Reach 1, Total 1474
Node 433, Reach 1, Total 1475
Node 434, Reach 1, Total 1476
Node 435, Reach 1, Total 1477
Node 436, Reach 1, Total 1478
Node 437, Reach 1, Total 1479
Node 438, Reach 1, Total 1480
Node 439, Reach 1, Total 1481
Node 440, Reach 1, Total 1482
Node 441, Reach 1, Total 1483
Node 442, Reach 1, Total 1484
Node 443, Reach 1, Total 1485
Node 444, Reach 1, Total 1486
Node 445, Reach 1, Total 1487
Node 446, Reach 1, Total 1488
Node 447, Reach 1, Total 1489
Node 448, Reach 1, Total 1490
Node 449, Reach 1, Total 1491
Node 450, Reach 1, Total 1492
Node 451, Reach 1, Total 1493
Node 452, Reach 1, Total 1494
Node 453, Reach 1, Total 1495
Node 454, Reach 1, Total 1496
Node 455, Reach 1, Total 1497
Node 456, Reach 1, Total 1498
Node 457, Reach 1, Total 1499
Node 458, Reach 1, Total 1500
Node 459, Reach 1, Total 1501
Node 460, Reach 1, Total 1502
Node 461, Reach 1, Total 1503
Node 462, Reach 1, Total 1504
Node 463, Reach 1, Total 1505
Node 464, Reach 1, Total 1506
Node 465, Reach 1, Total 1507
Node 466, Reach 1, Total 1508
Node 467, Reach 1, Total 1509
Node 468, Reach 1, Total 1510
Node 469, Reach 1, Total 1511
Node 470, Reach 1, Total 1512
Node 471, Reach 1, Total 1513
Node 472, Reach 1, Total 1514
Node 473, Reach 1, Total 1515
Node 474, Reach 1, Total 1516
Node 475, Reach 1, Total 1517
Node 476, Reach 1, Total 1518
Node 477, Reach 1, Total 1519
Node 478, Reach 1, Total 1520
Node 479, Reach 1, Total 1521
Node 480, Reach 1, Total 1522
Node 481, Reach 1, Total 1523
Node 482, Reach 1, Total 1524
Node 483, Reach 1, Total 1525
Node 484, Reach 1, Total 1526
Node 485, Reach 1, Total 1527
Node 486, Reach 1, Total 1528
Node 487, Reach 1, Total 1529
Node 488, Reach 1, Total 1530
Node 489, Reach 1, Total 1531
Node 490, Reach 1, Total 1532
Node 491, Reach 1, Total 1533
Node 492, Reach 1, Total 1534
Node 493, Reach 1, Total 1535
Node 494, Reach 1, Total 1536
Node 495, Reach 1, Total 1537
Node 496, Reach 1, Total 1538
Node 497, Reach 1, Total 1539
Node 498, Reach 1, Total 1540
Node 499, Reach 1, Total 1541
Node 500, Reach 1, Total 1542
Node 501, Reach 1, Total 1543
Node 502, Reach 1, Total 1544
Node 503, Reach 1, Total 1545
Node 504, Reach 1, Total 1546
Node 505, Reach 1, Total 1547
Node 506, Reach 1, Total 1548
Node 507, Reach 1, Total 1549
Node 508, Reach 1, Total 1550
Node 509, Reach 1, Total 1551
Node 510, Reach 1, Total 1552
Node 511, Reach 1, Total 1553
Node 512, Reach 1, Total 1554
Node 513, Reach 1, Total 1555
Node 514, Reach 1, Total 1556
Node 515, Reach 1, Total 1557
Node 516, Reach 1, Total 1558
Node 517, Reach 1, Total 1559
Node 518, Reach 1, Total 1560
Node 519, Reach 1, Total 1561
Node 520, Reach 1, Total 1562
Node 521, Reach 1, Total 1563
Node 522, Reach 1, Total 1564
Node 523, Reach 1, Total 1565
Node 524, Reach 1, Total 1566
Node 525, Reach 1, Total 1567
Node 526, Reach 1, Total 1568
Node 527, Reach 1, Total 1569
Node 528, Reach 1, Total 1570
Node 529, Reach 1, Total 1571
Node 530, Reach 1, Total 1572
Node 531, Reach 1, Total 1573
Node 532, Reach 1, Total 1574
Node 533, Reach 1, Total 1575
Node 534, Reach 1, Total 1576
Node 535, Reach 1, Total 1577
Node 536, Reach 1, Total 1578
Node 537, Reach 1, Total 1579
Node 538, Reach 1, Total 1580
Node 539, Reach 1, Total 1581
Node 540, Reach 1, Total 1582
Node 541, Reach 1, Total 1583
Node 542, Reach 1, Total 1584
Node 543, Reach 1, Total 1585
Node 544, Reach 1, Total 1586
Node 545, Reach 1, Total 1587
Node 546, Reach 1, Total 1588
Node 547, Reach 1, Total 1589
Node 548, Reach 1, Total 1590
Node 549, Reach 1, Total 1591
Node 550, Reach 1, Total 1592
Node 551, Reach 1, Total 1593
Node 552, Reach 1, Total 1594
Node 553, Reach 1, Total 1595
Node 554, Reach 1, Total 1596
Node 555, Reach 1, Total 1597
Node 556, Reach 1, Total 1598
Node 557, Reach 1, Total 1599
Node 558, Reach 1, Total 1600
Node 559, Reach 1, Total 1601
Node 560, Reach 1, Total 1602
Node 561, Reach 1, Total 1603
Node 562, Reach 1, Total 1604
Node 563, Reach 1, Total 1605
Node 564, Reach 1, Total 1606
Node 565, Reach 1, Total 1607
Node 566, Reach 1, Total 1608
Node 567, Reach 1, Total 1609
Node 568, Reach 1, Total 1610
Node 569, Reach 1, Total 1611
Node 570, Reach 1, Total 1612
Node 571, Reach 1, Total 1613
Node 572, Reach 1, Total 1614
Node 573, Reach 1, Total 1615
Node 574, Reach 1, Total 1616
Node 575, Reach 1, Total 1617
Node 576, Reach 1, Total 1618
Node 577, Reach 1, Total 1619
Node 578, Reach 1, Total 1620
Node 579, Reach 1, Total 1621
Node 580, Reach 1, Total 1622
Node 581, Reach 1, Total 1623
Node 582, Reach 1, Total 1624
Node 583, Reach 1, Total 1625
Node 584, Reach 1, Total 1626
Node 585, Reach 1, Total 1627
Node 586, Reach 1, Total 1628
Node 587, Reach 1, Total 1629
Node 588, Reach 1, Total 1630
Node 589, Reach 1, Total 1631
Node 590, Reach 1, Total 1632
Node 591, Reach 1, Total 1633
Node 592, Reach 1, Total 1634
Node 593, Reach 1, Total 1635
Node 594, Reach 1, Total 1636
Node 595, Reach 1, Total 1637
Node 596, Reach 1, Total 1638
Node 597, Reach 1, Total 1639
Node 598, Reach 1, Total 1640
Node 599, Reach 1, Total 1641
Node 600, Reach 1, Total 1642
Node 601, Reach 1, Total 1643
Node 602, Reach 1, Total 1644
Node 603, Reach 1, Total 1645
Node 604, Reach 1, Total 1646
Node 605, Reach 1, Total 1647
Node 606, Reach 1, Total 1648
Node 607, Reach 1, Total 1649
Node 608, Reach 1, Total 1650
Node 609, Reach 1, Total 1651
Node 610, Reach 1, Total 1652
Node 611, Reach 1, Total 1653
Node 612, Reach 1, Total 1654
Node 613, Reach 1, Total 1655
Node 614, Reach 1, Total 1656
Node 615, Reach 1, Total 1657
Node 616, Reach 1, Total 1658
Node 617, Reach 1, Total 1659
Node 618, Reach 1, Total 1660
Node 619, Reach 1, Total 1661
Node 620, Reach 1, Total 1662
Node 621, Reach 1, Total 1663
Node 622, Reach 1, Total 1664
Node 623, Reach 1, Total 1665
Node 624, Reach 1, Total 1666
Node 625, Reach 1, Total 1667
Node 626, Reach 1, Total 1668
Node 627, Reach 1, Total 1669
Node 628, Reach 1, Total 1670
Node 629, Reach 1, Total 1671
Node 630, Reach 1, Total 1672
Node 631, Reach 1, Total 1673
Node 632, Reach 1, Total 1674
Node 633, Reach 1, Total 1675
> names(reach) <- survey.names.extended
> reach.extended <- reach
> reach <- reach[names(reach) %in% survey.names]
> nomin.short.nw <- set.vertex.attribute(nomin.short.nw, "reach", reach)
> 
> # indegree outfactor (because those who receive many nominations give this back)
> idegree <- degree(nomin.short.nw, cmode = "indegree")
> nomin.short.nw <- set.vertex.attribute(nomin.short.nw, "ideg", idegree)
> 
> # look at reciprocal dyads
> table(nomin.mat * t(nomin.mat))

     0      1      2 
130309     10      2 
> 
> # network object for chapters
> chapters.nw <- network(chapters.mat, bipartite = FALSE, directed = FALSE, 
+     ignore.eval = FALSE, names.eval = "weight", loops = FALSE)
> set.vertex.attribute(chapters.nw, "socsci", attributes.short$socsci)
> set.vertex.attribute(chapters.nw, "gender", attributes.short$gender)
> set.vertex.attribute(chapters.nw, "nationality", attributes.short$nationality)
> set.vertex.attribute(chapters.nw, "school", attributes.short$school)
> set.vertex.attribute(chapters.nw, "expertise", attributes.short$expertise)
> set.vertex.attribute(chapters.nw, "education", attributes.short$education)
> set.vertex.attribute(chapters.nw, "country", attributes.short$country)
> set.vertex.attribute(chapters.nw, "employer", attributes.short$employer)
> set.vertex.attribute(chapters.nw, "phd", science)
> set.vertex.attribute(chapters.nw, "reach", reach)
> 
> # matrix for CLA sender
> cla.sender <- matrix(0, nrow = length(authortype), ncol = length(authortype))
> for (i in 1:nrow(cla.sender)) {
+   for (j in 1:ncol(cla.sender)) {
+     if (authortype[i] == "coordinating lead author") {
+       cla.sender[i, j] <- 1
+     }
+   }
+ }
> cla.interaction <- cla.sender * inst.cooc
> 
> # matrix for leader sender
> leader.sender <- matrix(0, nrow = length(authortype), ncol = length(authortype))
> for (i in 1:nrow(leader.sender)) {
+   for (j in 1:ncol(leader.sender)) {
+     if (leader[i] == 1) {
+       leader.sender[i, j] <- 1
+     }
+   }
+ }
> leader.interaction <- leader.sender * inst.cooc
> 
> 
> 
> ################################################################################
> # Network diagrams
> ################################################################################
> 
> coords.core <- plot(nomin.short.nw)
> dev.off()
null device 
          1 
> 
> # network diagram (Figure 1): all people in the survey
> pdf("output/figure_1.pdf", width = 7, height = 7)
> par(mar = c(0, 0, 1, 0))
> 
> ext.col <- extended.in.short
> ext.col[extended.in.short == TRUE] <- "white"
> ext.col[extended.in.short == FALSE] <- "black"
> 
> bothresp <- extended.in.short %*% t(extended.in.short)
> bothresp[bothresp == 1] <- 8  # gray
> bothresp[bothresp == 0] <- 1  # black
> 
> plot(nomin.extended.nw, edge.col = bothresp, arrowhead.cex = 0.5, 
+     edge.lwd = 1.0, vertex.cex = 0.6, vertex.col = ext.col
+ )
> 
> dev.off()
null device 
          1 
> 
> # network diagram: only respondents; elite memberships
> pdf("output/figure_2.pdf", width = 7, height = 7)
> plot(network(inst.mat, bipartite = TRUE), 
+      vertex.col = c(rep("white", nrow(inst.mat)), rep("gray", ncol(inst.mat))), 
+      vertex.cex = c(rep(0.7, nrow(inst.mat)), rep(1.0, ncol(inst.mat))), 
+      displayisolates = TRUE)
> dev.off()
null device 
          1 
> 
> # network diagram: only respondents; joint organizational memberships
> pdf("output/figure_3.pdf", width = 7, height = 7)
> mat.col <- inst.cooc
> mat.col[inst.cooc > 2] <- "#000000"
> mat.col[inst.cooc == 2] <- "#8B8B8B"
> mat.col[inst.cooc == 1] <- "#BFBFBF"
> mat.col[inst.cooc == 0] <- "#E6E6E6"
> plot(nomin.short.nw, edge.col = mat.col, vertex.col = "white", 
+     displayisolates = TRUE, coord = coords.core, 
+     arrowhead.cex = 0.5, edge.lwd = 1.0, vertex.cex = 0.7)
> dev.off()
null device 
          1 
> 
> 
> ################################################################################
> # Outdegree centrality of reachability matrix and cross-table
> ################################################################################
> 
> ocrm <- rowSums(reachability(nomin.mat.extended))
Node 1, Reach 12, Total 12
Node 2, Reach 2, Total 14
Node 3, Reach 8, Total 22
Node 4, Reach 6, Total 28
Node 5, Reach 5, Total 33
Node 6, Reach 14, Total 47
Node 7, Reach 5, Total 52
Node 8, Reach 100, Total 152
Node 9, Reach 20, Total 172
Node 10, Reach 6, Total 178
Node 11, Reach 1, Total 179
Node 12, Reach 4, Total 183
Node 13, Reach 2, Total 185
Node 14, Reach 2, Total 187
Node 15, Reach 2, Total 189
Node 16, Reach 2, Total 191
Node 17, Reach 2, Total 193
Node 18, Reach 4, Total 197
Node 19, Reach 2, Total 199
Node 20, Reach 12, Total 211
Node 21, Reach 5, Total 216
Node 22, Reach 4, Total 220
Node 23, Reach 15, Total 235
Node 24, Reach 5, Total 240
Node 25, Reach 19, Total 259
Node 26, Reach 8, Total 267
Node 27, Reach 2, Total 269
Node 28, Reach 7, Total 276
Node 29, Reach 3, Total 279
Node 30, Reach 3, Total 282
Node 31, Reach 3, Total 285
Node 32, Reach 12, Total 297
Node 33, Reach 9, Total 306
Node 34, Reach 13, Total 319
Node 35, Reach 5, Total 324
Node 36, Reach 5, Total 329
Node 37, Reach 2, Total 331
Node 38, Reach 3, Total 334
Node 39, Reach 24, Total 358
Node 40, Reach 10, Total 368
Node 41, Reach 10, Total 378
Node 42, Reach 3, Total 381
Node 43, Reach 3, Total 384
Node 44, Reach 2, Total 386
Node 45, Reach 2, Total 388
Node 46, Reach 2, Total 390
Node 47, Reach 7, Total 397
Node 48, Reach 2, Total 399
Node 49, Reach 2, Total 401
Node 50, Reach 7, Total 408
Node 51, Reach 3, Total 411
Node 52, Reach 12, Total 423
Node 53, Reach 2, Total 425
Node 54, Reach 2, Total 427
Node 55, Reach 12, Total 439
Node 56, Reach 2, Total 441
Node 57, Reach 5, Total 446
Node 58, Reach 16, Total 462
Node 59, Reach 4, Total 466
Node 60, Reach 15, Total 481
Node 61, Reach 4, Total 485
Node 62, Reach 3, Total 488
Node 63, Reach 22, Total 510
Node 64, Reach 2, Total 512
Node 65, Reach 25, Total 537
Node 66, Reach 2, Total 539
Node 67, Reach 7, Total 546
Node 68, Reach 4, Total 550
Node 69, Reach 2, Total 552
Node 70, Reach 2, Total 554
Node 71, Reach 2, Total 556
Node 72, Reach 9, Total 565
Node 73, Reach 2, Total 567
Node 74, Reach 21, Total 588
Node 75, Reach 4, Total 592
Node 76, Reach 2, Total 594
Node 77, Reach 2, Total 596
Node 78, Reach 2, Total 598
Node 79, Reach 2, Total 600
Node 80, Reach 2, Total 602
Node 81, Reach 2, Total 604
Node 82, Reach 2, Total 606
Node 83, Reach 2, Total 608
Node 84, Reach 2, Total 610
Node 85, Reach 4, Total 614
Node 86, Reach 3, Total 617
Node 87, Reach 2, Total 619
Node 88, Reach 6, Total 625
Node 89, Reach 12, Total 637
Node 90, Reach 3, Total 640
Node 91, Reach 2, Total 642
Node 92, Reach 3, Total 645
Node 93, Reach 3, Total 648
Node 94, Reach 2, Total 650
Node 95, Reach 2, Total 652
Node 96, Reach 3, Total 655
Node 97, Reach 8, Total 663
Node 98, Reach 2, Total 665
Node 99, Reach 3, Total 668
Node 100, Reach 2, Total 670
Node 101, Reach 2, Total 672
Node 102, Reach 2, Total 674
Node 103, Reach 2, Total 676
Node 104, Reach 14, Total 690
Node 105, Reach 2, Total 692
Node 106, Reach 9, Total 701
Node 107, Reach 9, Total 710
Node 108, Reach 4, Total 714
Node 109, Reach 2, Total 716
Node 110, Reach 164, Total 880
Node 111, Reach 2, Total 882
Node 112, Reach 6, Total 888
Node 113, Reach 5, Total 893
Node 114, Reach 11, Total 904
Node 115, Reach 2, Total 906
Node 116, Reach 6, Total 912
Node 117, Reach 2, Total 914
Node 118, Reach 5, Total 919
Node 119, Reach 2, Total 921
Node 120, Reach 4, Total 925
Node 121, Reach 3, Total 928
Node 122, Reach 2, Total 930
Node 123, Reach 3, Total 933
Node 124, Reach 7, Total 940
Node 125, Reach 2, Total 942
Node 126, Reach 2, Total 944
Node 127, Reach 3, Total 947
Node 128, Reach 2, Total 949
Node 129, Reach 11, Total 960
Node 130, Reach 2, Total 962
Node 131, Reach 13, Total 975
Node 132, Reach 2, Total 977
Node 133, Reach 10, Total 987
Node 134, Reach 5, Total 992
Node 135, Reach 7, Total 999
Node 136, Reach 2, Total 1001
Node 137, Reach 4, Total 1005
Node 138, Reach 2, Total 1007
Node 139, Reach 4, Total 1011
Node 140, Reach 2, Total 1013
Node 141, Reach 2, Total 1015
Node 142, Reach 7, Total 1022
Node 143, Reach 2, Total 1024
Node 144, Reach 2, Total 1026
Node 145, Reach 2, Total 1028
Node 146, Reach 6, Total 1034
Node 147, Reach 3, Total 1037
Node 148, Reach 12, Total 1049
Node 149, Reach 3, Total 1052
Node 150, Reach 7, Total 1059
Node 151, Reach 2, Total 1061
Node 152, Reach 12, Total 1073
Node 153, Reach 22, Total 1095
Node 154, Reach 9, Total 1104
Node 155, Reach 5, Total 1109
Node 156, Reach 2, Total 1111
Node 157, Reach 2, Total 1113
Node 158, Reach 11, Total 1124
Node 159, Reach 5, Total 1129
Node 160, Reach 12, Total 1141
Node 161, Reach 4, Total 1145
Node 162, Reach 5, Total 1150
Node 163, Reach 4, Total 1154
Node 164, Reach 2, Total 1156
Node 165, Reach 5, Total 1161
Node 166, Reach 2, Total 1163
Node 167, Reach 4, Total 1167
Node 168, Reach 2, Total 1169
Node 169, Reach 2, Total 1171
Node 170, Reach 11, Total 1182
Node 171, Reach 2, Total 1184
Node 172, Reach 5, Total 1189
Node 173, Reach 3, Total 1192
Node 174, Reach 3, Total 1195
Node 175, Reach 2, Total 1197
Node 176, Reach 6, Total 1203
Node 177, Reach 9, Total 1212
Node 178, Reach 8, Total 1220
Node 179, Reach 1, Total 1221
Node 180, Reach 1, Total 1222
Node 181, Reach 1, Total 1223
Node 182, Reach 1, Total 1224
Node 183, Reach 1, Total 1225
Node 184, Reach 1, Total 1226
Node 185, Reach 1, Total 1227
Node 186, Reach 1, Total 1228
Node 187, Reach 1, Total 1229
Node 188, Reach 1, Total 1230
Node 189, Reach 1, Total 1231
Node 190, Reach 1, Total 1232
Node 191, Reach 1, Total 1233
Node 192, Reach 1, Total 1234
Node 193, Reach 1, Total 1235
Node 194, Reach 1, Total 1236
Node 195, Reach 1, Total 1237
Node 196, Reach 1, Total 1238
Node 197, Reach 1, Total 1239
Node 198, Reach 1, Total 1240
Node 199, Reach 1, Total 1241
Node 200, Reach 1, Total 1242
Node 201, Reach 1, Total 1243
Node 202, Reach 1, Total 1244
Node 203, Reach 1, Total 1245
Node 204, Reach 1, Total 1246
Node 205, Reach 1, Total 1247
Node 206, Reach 1, Total 1248
Node 207, Reach 1, Total 1249
Node 208, Reach 1, Total 1250
Node 209, Reach 1, Total 1251
Node 210, Reach 1, Total 1252
Node 211, Reach 1, Total 1253
Node 212, Reach 1, Total 1254
Node 213, Reach 1, Total 1255
Node 214, Reach 1, Total 1256
Node 215, Reach 1, Total 1257
Node 216, Reach 1, Total 1258
Node 217, Reach 1, Total 1259
Node 218, Reach 1, Total 1260
Node 219, Reach 1, Total 1261
Node 220, Reach 1, Total 1262
Node 221, Reach 1, Total 1263
Node 222, Reach 1, Total 1264
Node 223, Reach 1, Total 1265
Node 224, Reach 1, Total 1266
Node 225, Reach 1, Total 1267
Node 226, Reach 1, Total 1268
Node 227, Reach 1, Total 1269
Node 228, Reach 1, Total 1270
Node 229, Reach 1, Total 1271
Node 230, Reach 1, Total 1272
Node 231, Reach 1, Total 1273
Node 232, Reach 1, Total 1274
Node 233, Reach 1, Total 1275
Node 234, Reach 1, Total 1276
Node 235, Reach 1, Total 1277
Node 236, Reach 1, Total 1278
Node 237, Reach 1, Total 1279
Node 238, Reach 1, Total 1280
Node 239, Reach 1, Total 1281
Node 240, Reach 1, Total 1282
Node 241, Reach 1, Total 1283
Node 242, Reach 1, Total 1284
Node 243, Reach 1, Total 1285
Node 244, Reach 1, Total 1286
Node 245, Reach 1, Total 1287
Node 246, Reach 1, Total 1288
Node 247, Reach 1, Total 1289
Node 248, Reach 1, Total 1290
Node 249, Reach 1, Total 1291
Node 250, Reach 1, Total 1292
Node 251, Reach 1, Total 1293
Node 252, Reach 1, Total 1294
Node 253, Reach 1, Total 1295
Node 254, Reach 1, Total 1296
Node 255, Reach 1, Total 1297
Node 256, Reach 1, Total 1298
Node 257, Reach 1, Total 1299
Node 258, Reach 1, Total 1300
Node 259, Reach 1, Total 1301
Node 260, Reach 1, Total 1302
Node 261, Reach 1, Total 1303
Node 262, Reach 1, Total 1304
Node 263, Reach 1, Total 1305
Node 264, Reach 1, Total 1306
Node 265, Reach 1, Total 1307
Node 266, Reach 1, Total 1308
Node 267, Reach 1, Total 1309
Node 268, Reach 1, Total 1310
Node 269, Reach 1, Total 1311
Node 270, Reach 1, Total 1312
Node 271, Reach 1, Total 1313
Node 272, Reach 1, Total 1314
Node 273, Reach 1, Total 1315
Node 274, Reach 1, Total 1316
Node 275, Reach 1, Total 1317
Node 276, Reach 1, Total 1318
Node 277, Reach 1, Total 1319
Node 278, Reach 1, Total 1320
Node 279, Reach 1, Total 1321
Node 280, Reach 1, Total 1322
Node 281, Reach 1, Total 1323
Node 282, Reach 1, Total 1324
Node 283, Reach 1, Total 1325
Node 284, Reach 1, Total 1326
Node 285, Reach 1, Total 1327
Node 286, Reach 1, Total 1328
Node 287, Reach 1, Total 1329
Node 288, Reach 1, Total 1330
Node 289, Reach 1, Total 1331
Node 290, Reach 1, Total 1332
Node 291, Reach 1, Total 1333
Node 292, Reach 1, Total 1334
Node 293, Reach 1, Total 1335
Node 294, Reach 1, Total 1336
Node 295, Reach 1, Total 1337
Node 296, Reach 1, Total 1338
Node 297, Reach 1, Total 1339
Node 298, Reach 1, Total 1340
Node 299, Reach 1, Total 1341
Node 300, Reach 1, Total 1342
Node 301, Reach 1, Total 1343
Node 302, Reach 1, Total 1344
Node 303, Reach 1, Total 1345
Node 304, Reach 1, Total 1346
Node 305, Reach 1, Total 1347
Node 306, Reach 1, Total 1348
Node 307, Reach 1, Total 1349
Node 308, Reach 1, Total 1350
Node 309, Reach 1, Total 1351
Node 310, Reach 1, Total 1352
Node 311, Reach 1, Total 1353
Node 312, Reach 1, Total 1354
Node 313, Reach 1, Total 1355
Node 314, Reach 1, Total 1356
Node 315, Reach 1, Total 1357
Node 316, Reach 1, Total 1358
Node 317, Reach 1, Total 1359
Node 318, Reach 1, Total 1360
Node 319, Reach 1, Total 1361
Node 320, Reach 1, Total 1362
Node 321, Reach 1, Total 1363
Node 322, Reach 1, Total 1364
Node 323, Reach 1, Total 1365
Node 324, Reach 1, Total 1366
Node 325, Reach 1, Total 1367
Node 326, Reach 1, Total 1368
Node 327, Reach 1, Total 1369
Node 328, Reach 1, Total 1370
Node 329, Reach 1, Total 1371
Node 330, Reach 1, Total 1372
Node 331, Reach 1, Total 1373
Node 332, Reach 1, Total 1374
Node 333, Reach 1, Total 1375
Node 334, Reach 1, Total 1376
Node 335, Reach 1, Total 1377
Node 336, Reach 1, Total 1378
Node 337, Reach 1, Total 1379
Node 338, Reach 1, Total 1380
Node 339, Reach 1, Total 1381
Node 340, Reach 1, Total 1382
Node 341, Reach 1, Total 1383
Node 342, Reach 1, Total 1384
Node 343, Reach 1, Total 1385
Node 344, Reach 1, Total 1386
Node 345, Reach 1, Total 1387
Node 346, Reach 1, Total 1388
Node 347, Reach 1, Total 1389
Node 348, Reach 1, Total 1390
Node 349, Reach 1, Total 1391
Node 350, Reach 1, Total 1392
Node 351, Reach 1, Total 1393
Node 352, Reach 1, Total 1394
Node 353, Reach 1, Total 1395
Node 354, Reach 1, Total 1396
Node 355, Reach 1, Total 1397
Node 356, Reach 1, Total 1398
Node 357, Reach 1, Total 1399
Node 358, Reach 1, Total 1400
Node 359, Reach 1, Total 1401
Node 360, Reach 1, Total 1402
Node 361, Reach 1, Total 1403
Node 362, Reach 1, Total 1404
Node 363, Reach 1, Total 1405
Node 364, Reach 1, Total 1406
Node 365, Reach 1, Total 1407
Node 366, Reach 1, Total 1408
Node 367, Reach 1, Total 1409
Node 368, Reach 1, Total 1410
Node 369, Reach 1, Total 1411
Node 370, Reach 1, Total 1412
Node 371, Reach 1, Total 1413
Node 372, Reach 1, Total 1414
Node 373, Reach 1, Total 1415
Node 374, Reach 1, Total 1416
Node 375, Reach 1, Total 1417
Node 376, Reach 1, Total 1418
Node 377, Reach 1, Total 1419
Node 378, Reach 1, Total 1420
Node 379, Reach 1, Total 1421
Node 380, Reach 1, Total 1422
Node 381, Reach 1, Total 1423
Node 382, Reach 1, Total 1424
Node 383, Reach 1, Total 1425
Node 384, Reach 1, Total 1426
Node 385, Reach 1, Total 1427
Node 386, Reach 1, Total 1428
Node 387, Reach 1, Total 1429
Node 388, Reach 1, Total 1430
Node 389, Reach 1, Total 1431
Node 390, Reach 1, Total 1432
Node 391, Reach 1, Total 1433
Node 392, Reach 1, Total 1434
Node 393, Reach 1, Total 1435
Node 394, Reach 1, Total 1436
Node 395, Reach 1, Total 1437
Node 396, Reach 1, Total 1438
Node 397, Reach 1, Total 1439
Node 398, Reach 1, Total 1440
Node 399, Reach 1, Total 1441
Node 400, Reach 1, Total 1442
Node 401, Reach 1, Total 1443
Node 402, Reach 1, Total 1444
Node 403, Reach 1, Total 1445
Node 404, Reach 1, Total 1446
Node 405, Reach 1, Total 1447
Node 406, Reach 1, Total 1448
Node 407, Reach 1, Total 1449
Node 408, Reach 1, Total 1450
Node 409, Reach 1, Total 1451
Node 410, Reach 1, Total 1452
Node 411, Reach 1, Total 1453
Node 412, Reach 1, Total 1454
Node 413, Reach 1, Total 1455
Node 414, Reach 1, Total 1456
Node 415, Reach 1, Total 1457
Node 416, Reach 1, Total 1458
Node 417, Reach 1, Total 1459
Node 418, Reach 1, Total 1460
Node 419, Reach 1, Total 1461
Node 420, Reach 1, Total 1462
Node 421, Reach 1, Total 1463
Node 422, Reach 1, Total 1464
Node 423, Reach 1, Total 1465
Node 424, Reach 1, Total 1466
Node 425, Reach 1, Total 1467
Node 426, Reach 1, Total 1468
Node 427, Reach 1, Total 1469
Node 428, Reach 1, Total 1470
Node 429, Reach 1, Total 1471
Node 430, Reach 1, Total 1472
Node 431, Reach 1, Total 1473
Node 432, Reach 1, Total 1474
Node 433, Reach 1, Total 1475
Node 434, Reach 1, Total 1476
Node 435, Reach 1, Total 1477
Node 436, Reach 1, Total 1478
Node 437, Reach 1, Total 1479
Node 438, Reach 1, Total 1480
Node 439, Reach 1, Total 1481
Node 440, Reach 1, Total 1482
Node 441, Reach 1, Total 1483
Node 442, Reach 1, Total 1484
Node 443, Reach 1, Total 1485
Node 444, Reach 1, Total 1486
Node 445, Reach 1, Total 1487
Node 446, Reach 1, Total 1488
Node 447, Reach 1, Total 1489
Node 448, Reach 1, Total 1490
Node 449, Reach 1, Total 1491
Node 450, Reach 1, Total 1492
Node 451, Reach 1, Total 1493
Node 452, Reach 1, Total 1494
Node 453, Reach 1, Total 1495
Node 454, Reach 1, Total 1496
Node 455, Reach 1, Total 1497
Node 456, Reach 1, Total 1498
Node 457, Reach 1, Total 1499
Node 458, Reach 1, Total 1500
Node 459, Reach 1, Total 1501
Node 460, Reach 1, Total 1502
Node 461, Reach 1, Total 1503
Node 462, Reach 1, Total 1504
Node 463, Reach 1, Total 1505
Node 464, Reach 1, Total 1506
Node 465, Reach 1, Total 1507
Node 466, Reach 1, Total 1508
Node 467, Reach 1, Total 1509
Node 468, Reach 1, Total 1510
Node 469, Reach 1, Total 1511
Node 470, Reach 1, Total 1512
Node 471, Reach 1, Total 1513
Node 472, Reach 1, Total 1514
Node 473, Reach 1, Total 1515
Node 474, Reach 1, Total 1516
Node 475, Reach 1, Total 1517
Node 476, Reach 1, Total 1518
Node 477, Reach 1, Total 1519
Node 478, Reach 1, Total 1520
Node 479, Reach 1, Total 1521
Node 480, Reach 1, Total 1522
Node 481, Reach 1, Total 1523
Node 482, Reach 1, Total 1524
Node 483, Reach 1, Total 1525
Node 484, Reach 1, Total 1526
Node 485, Reach 1, Total 1527
Node 486, Reach 1, Total 1528
Node 487, Reach 1, Total 1529
Node 488, Reach 1, Total 1530
Node 489, Reach 1, Total 1531
Node 490, Reach 1, Total 1532
Node 491, Reach 1, Total 1533
Node 492, Reach 1, Total 1534
Node 493, Reach 1, Total 1535
Node 494, Reach 1, Total 1536
Node 495, Reach 1, Total 1537
Node 496, Reach 1, Total 1538
Node 497, Reach 1, Total 1539
Node 498, Reach 1, Total 1540
Node 499, Reach 1, Total 1541
Node 500, Reach 1, Total 1542
Node 501, Reach 1, Total 1543
Node 502, Reach 1, Total 1544
Node 503, Reach 1, Total 1545
Node 504, Reach 1, Total 1546
Node 505, Reach 1, Total 1547
Node 506, Reach 1, Total 1548
Node 507, Reach 1, Total 1549
Node 508, Reach 1, Total 1550
Node 509, Reach 1, Total 1551
Node 510, Reach 1, Total 1552
Node 511, Reach 1, Total 1553
Node 512, Reach 1, Total 1554
Node 513, Reach 1, Total 1555
Node 514, Reach 1, Total 1556
Node 515, Reach 1, Total 1557
Node 516, Reach 1, Total 1558
Node 517, Reach 1, Total 1559
Node 518, Reach 1, Total 1560
Node 519, Reach 1, Total 1561
Node 520, Reach 1, Total 1562
Node 521, Reach 1, Total 1563
Node 522, Reach 1, Total 1564
Node 523, Reach 1, Total 1565
Node 524, Reach 1, Total 1566
Node 525, Reach 1, Total 1567
Node 526, Reach 1, Total 1568
Node 527, Reach 1, Total 1569
Node 528, Reach 1, Total 1570
Node 529, Reach 1, Total 1571
Node 530, Reach 1, Total 1572
Node 531, Reach 1, Total 1573
Node 532, Reach 1, Total 1574
Node 533, Reach 1, Total 1575
Node 534, Reach 1, Total 1576
Node 535, Reach 1, Total 1577
Node 536, Reach 1, Total 1578
Node 537, Reach 1, Total 1579
Node 538, Reach 1, Total 1580
Node 539, Reach 1, Total 1581
Node 540, Reach 1, Total 1582
Node 541, Reach 1, Total 1583
Node 542, Reach 1, Total 1584
Node 543, Reach 1, Total 1585
Node 544, Reach 1, Total 1586
Node 545, Reach 1, Total 1587
Node 546, Reach 1, Total 1588
Node 547, Reach 1, Total 1589
Node 548, Reach 1, Total 1590
Node 549, Reach 1, Total 1591
Node 550, Reach 1, Total 1592
Node 551, Reach 1, Total 1593
Node 552, Reach 1, Total 1594
Node 553, Reach 1, Total 1595
Node 554, Reach 1, Total 1596
Node 555, Reach 1, Total 1597
Node 556, Reach 1, Total 1598
Node 557, Reach 1, Total 1599
Node 558, Reach 1, Total 1600
Node 559, Reach 1, Total 1601
Node 560, Reach 1, Total 1602
Node 561, Reach 1, Total 1603
Node 562, Reach 1, Total 1604
Node 563, Reach 1, Total 1605
Node 564, Reach 1, Total 1606
Node 565, Reach 1, Total 1607
Node 566, Reach 1, Total 1608
Node 567, Reach 1, Total 1609
Node 568, Reach 1, Total 1610
Node 569, Reach 1, Total 1611
Node 570, Reach 1, Total 1612
Node 571, Reach 1, Total 1613
Node 572, Reach 1, Total 1614
Node 573, Reach 1, Total 1615
Node 574, Reach 1, Total 1616
Node 575, Reach 1, Total 1617
Node 576, Reach 1, Total 1618
Node 577, Reach 1, Total 1619
Node 578, Reach 1, Total 1620
Node 579, Reach 1, Total 1621
Node 580, Reach 1, Total 1622
Node 581, Reach 1, Total 1623
Node 582, Reach 1, Total 1624
Node 583, Reach 1, Total 1625
Node 584, Reach 1, Total 1626
Node 585, Reach 1, Total 1627
Node 586, Reach 1, Total 1628
Node 587, Reach 1, Total 1629
Node 588, Reach 1, Total 1630
Node 589, Reach 1, Total 1631
Node 590, Reach 1, Total 1632
Node 591, Reach 1, Total 1633
Node 592, Reach 1, Total 1634
Node 593, Reach 1, Total 1635
Node 594, Reach 1, Total 1636
Node 595, Reach 1, Total 1637
Node 596, Reach 1, Total 1638
Node 597, Reach 1, Total 1639
Node 598, Reach 1, Total 1640
Node 599, Reach 1, Total 1641
Node 600, Reach 1, Total 1642
Node 601, Reach 1, Total 1643
Node 602, Reach 1, Total 1644
Node 603, Reach 1, Total 1645
Node 604, Reach 1, Total 1646
Node 605, Reach 1, Total 1647
Node 606, Reach 1, Total 1648
Node 607, Reach 1, Total 1649
Node 608, Reach 1, Total 1650
Node 609, Reach 1, Total 1651
Node 610, Reach 1, Total 1652
Node 611, Reach 1, Total 1653
Node 612, Reach 1, Total 1654
Node 613, Reach 1, Total 1655
Node 614, Reach 1, Total 1656
Node 615, Reach 1, Total 1657
Node 616, Reach 1, Total 1658
Node 617, Reach 1, Total 1659
Node 618, Reach 1, Total 1660
Node 619, Reach 1, Total 1661
Node 620, Reach 1, Total 1662
Node 621, Reach 1, Total 1663
Node 622, Reach 1, Total 1664
Node 623, Reach 1, Total 1665
Node 624, Reach 1, Total 1666
Node 625, Reach 1, Total 1667
Node 626, Reach 1, Total 1668
Node 627, Reach 1, Total 1669
Node 628, Reach 1, Total 1670
Node 629, Reach 1, Total 1671
Node 630, Reach 1, Total 1672
Node 631, Reach 1, Total 1673
Node 632, Reach 1, Total 1674
Node 633, Reach 1, Total 1675
> names(ocrm) <- survey.names.extended
> ocrm <- sort(ocrm, decreasing = TRUE)[1:30]
> write.csv2(ocrm, file = "output/reachability.csv")
> 
> crosstable <- matrix(NA, nrow = 6, ncol = 5)
> rownames(crosstable) <- c("All members", 
+                           "Coordinating Lead Authors", 
+                           "Lead Authors", 
+                           "Director", 
+                           "Assessment Panel", 
+                           "Working Group Editors")
> colnames(crosstable) <- c("N", 
+                           "Nomination outdegree", 
+                           "SD", 
+                           "Reachability outdegree", 
+                           "SD")
> 
> crosstable[1, 1] <- length(survey.names.extended)
> crosstable[1, 2] <- mean(degree(nomin.extended.nw, cmode = "outdegree"))
> crosstable[1, 3] <- sd(degree(nomin.extended.nw, cmode = "outdegree"))
> crosstable[1, 4] <- mean(reach.extended)
> crosstable[1, 5] <- sd(reach.extended)
> 
> crosstable[2, 1] <- length(survey.names.extended[cla])
> crosstable[2, 2] <- mean(degree(nomin.extended.nw, cmode = "outdegree")[cla])
> crosstable[2, 3] <- sd(degree(nomin.extended.nw, cmode = "outdegree")[cla])
> crosstable[2, 4] <- mean(reach.extended[cla])
> crosstable[2, 5] <- sd(reach.extended[cla])
> 
> crosstable[3, 1] <- length(survey.names.extended[la])
> crosstable[3, 2] <- mean(degree(nomin.extended.nw, cmode = "outdegree")[la])
> crosstable[3, 3] <- sd(degree(nomin.extended.nw, cmode = "outdegree")[la])
> crosstable[3, 4] <- mean(reach.extended[la])
> crosstable[3, 5] <- sd(reach.extended[la])
> 
> crosstable[4, 1] <- length(survey.names.extended[director])
> crosstable[4, 2] <- mean(degree(nomin.extended.nw, cmode = "outdegree")[director])
> crosstable[4, 3] <- 0
> crosstable[4, 4] <- mean(reach.extended[director])
> crosstable[4, 5] <- 0
> 
> crosstable[5, 1] <- length(survey.names.extended[asspanel])
> crosstable[5, 2] <- mean(degree(nomin.extended.nw, cmode = "outdegree")[asspanel])
> crosstable[5, 3] <- sd(degree(nomin.extended.nw, cmode = "outdegree")[asspanel])
> crosstable[5, 4] <- mean(reach.extended[asspanel])
> crosstable[5, 5] <- sd(reach.extended[asspanel])
> 
> crosstable[6, 1] <- length(survey.names.extended[wg.editor])
> crosstable[6, 2] <- mean(degree(nomin.extended.nw, cmode = "outdegree")[wg.editor])
> crosstable[6, 3] <- sd(degree(nomin.extended.nw, cmode = "outdegree")[wg.editor])
> crosstable[6, 4] <- mean(reach.extended[wg.editor])
> crosstable[6, 5] <- sd(reach.extended[wg.editor])
> 
> sink("output/crosstable.tex")
> xtable(crosstable, 
+        caption = "Nomination outdegree and reachability outdegree of leading members", 
+        label = "crosstable", 
+        align = "lrrrrr")
> sink()
> 
> 
> ################################################################################
> # ERGM estimation and GOF assessment
> ################################################################################
> 
> # ERGM: only intercept; null model
> model1 <- ergm(nomin.short.nw ~ 
+     edges, 
+     control = control.ergm(MCMC.interval = 2000, MCMC.burnin = 30000, 
+     MCMC.samplesize = 15000)
+ )
Evaluating log-likelihood at the estimate. 
> summary(model1)

==========================
Summary of model fit
==========================

Formula:   nomin.short.nw ~ edges

Iterations:  8 out of 20 

Monte Carlo MLE Results:
      Estimate Std. Error MCMC % p-value    
edges -6.71753    0.07985      0  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 180163  on 129960  degrees of freedom
 Residual Deviance:   2423  on 129959  degrees of freedom
 
AIC: 2425    BIC: 2435    (Smaller is better.) 
> 
> # ERGM: only intercept and covariates; no endogenous processes
> model2 <- ergm(nomin.short.nw ~ 
+     edges + 
+     nodematch("nationality") + 
+     nodematch("employer") + 
+     nodeofactor("gender") + 
+     nodeifactor("gender") + 
+     nodemix("gender", base = -4) + 
+     nodeicov("inst.num") + 
+     nodeocov("inst.num") + 
+     edgecov(inst.cooc) + 
+     edgecov(inst.cooc.sq) + 
+     nodematch("expertise") + 
+     edgecov(chapters.mat.binary) + 
+     nodematch("education") + 
+     nodeifactor("phd") + 
+     nodeofactor("phd") + 
+     nodeifactor("socsci") + 
+     nodeofactor("socsci") + 
+     nodematch("socsci") + 
+     nodeofactor("authortype", base = -2) + 
+     edgecov(cla.interaction) + 
+     nodeofactor("leader") + 
+     edgecov(leader.interaction), 
+     control = control.ergm(MCMC.interval = 2000, MCMC.burnin = 30000, 
+     MCMC.samplesize = 15000)
+ )
Evaluating log-likelihood at the estimate. 
> summary(model2)

==========================
Summary of model fit
==========================

Formula:   nomin.short.nw ~ edges + nodematch("nationality") + nodematch("employer") + 
    nodeofactor("gender") + nodeifactor("gender") + nodemix("gender", 
    base = -4) + nodeicov("inst.num") + nodeocov("inst.num") + 
    edgecov(inst.cooc) + edgecov(inst.cooc.sq) + nodematch("expertise") + 
    edgecov(chapters.mat.binary) + nodematch("education") + nodeifactor("phd") + 
    nodeofactor("phd") + nodeifactor("socsci") + nodeofactor("socsci") + 
    nodematch("socsci") + nodeofactor("authortype", base = -2) + 
    edgecov(cla.interaction) + nodeofactor("leader") + edgecov(leader.interaction)

Iterations:  9 out of 20 

Monte Carlo MLE Results:
                                                 Estimate Std. Error MCMC %
edges                                           -8.894495   0.407196      0
nodematch.nationality                            1.688653   0.182708      0
nodematch.employer                               3.109893   0.424393      0
nodeofactor.gender.Male                          0.638906   0.323057      0
nodeifactor.gender.Male                          0.217787   0.358200      0
mix.gender.Male.Male                            -0.488020   0.401934      0
nodeicov.inst.num                               -0.150630   0.047656      0
nodeocov.inst.num                               -0.010908   0.031224      0
edgecov.inst.cooc                                1.376732   0.232180      0
edgecov.inst.cooc.sq                            -0.167778   0.048816      0
nodematch.expertise                              0.621750   0.217821      0
edgecov.chapters.mat.binary                      3.326053   0.172247      0
nodematch.education                             -0.339284   0.194270      0
nodeifactor.phd.1                               -0.062927   0.199080      0
nodeofactor.phd.1                                0.455175   0.233152      0
nodeifactor.socsci.yes                           0.062111   0.190515      0
nodeofactor.socsci.yes                           0.303129   0.194880      0
nodematch.socsci                                 0.088226   0.196433      0
nodeofactor.authortype.coordinating lead author  1.637365   0.240482      0
edgecov.cla.interaction                         -0.163568   0.230139      0
nodeofactor.leader.1                             1.616595   0.286528      0
edgecov.leader.interaction                       0.008779   0.201103      0
                                                 p-value    
edges                                            < 1e-04 ***
nodematch.nationality                            < 1e-04 ***
nodematch.employer                               < 1e-04 ***
nodeofactor.gender.Male                         0.047966 *  
nodeifactor.gender.Male                         0.543187    
mix.gender.Male.Male                            0.224681    
nodeicov.inst.num                               0.001574 ** 
nodeocov.inst.num                               0.726830    
edgecov.inst.cooc                                < 1e-04 ***
edgecov.inst.cooc.sq                            0.000588 ***
nodematch.expertise                             0.004312 ** 
edgecov.chapters.mat.binary                      < 1e-04 ***
nodematch.education                             0.080733 .  
nodeifactor.phd.1                               0.751934    
nodeofactor.phd.1                               0.050908 .  
nodeifactor.socsci.yes                          0.744412    
nodeofactor.socsci.yes                          0.119838    
nodematch.socsci                                0.653332    
nodeofactor.authortype.coordinating lead author  < 1e-04 ***
edgecov.cla.interaction                         0.477250    
nodeofactor.leader.1                             < 1e-04 ***
edgecov.leader.interaction                      0.965181    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 180163  on 129960  degrees of freedom
 Residual Deviance:   1775  on 129938  degrees of freedom
 
AIC: 1819    BIC: 2034    (Smaller is better.) 
> 
> # ERGM: full specification
> model3 <- ergm(nomin.short.nw ~ 
+     edges + 
+     istar(2) + 
+     ostar(2:3) + 
+     twopath + 
+     isolates + 
+     nodematch("nationality") + 
+     nodematch("employer") + 
+     nodeofactor("gender") + 
+     nodeifactor("gender") + 
+     nodemix("gender", base = -4) + 
+     nodeicov("inst.num") + 
+     nodeocov("inst.num") + 
+     edgecov(inst.cooc) + 
+     edgecov(inst.cooc.sq) + 
+     nodematch("expertise") + 
+     edgecov(chapters.mat.binary) + 
+     nodematch("education") + 
+     nodeifactor("phd") + 
+     nodeofactor("phd") + 
+     nodeifactor("socsci") + 
+     nodeofactor("socsci") + 
+     nodematch("socsci") + 
+     nodeofactor("authortype", base = -2) + 
+     edgecov(cla.interaction) + 
+     nodeofactor("leader") + 
+     edgecov(leader.interaction)
+     , control = control.ergm(MCMC.interval = 2000, MCMC.burnin = 30000, 
+     MCMC.samplesize = 15000)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 20: 
The log-likelihood improved by 4.421 
Iteration 2 of at most 20: 
The log-likelihood improved by 3.318 
Iteration 3 of at most 20: 
The log-likelihood improved by 1.978 
Step length converged once. Increasing MCMC sample size.
Iteration 4 of at most 20: 
The log-likelihood improved by 0.1857 
Step length converged twice. Stopping.
Evaluating log-likelihood at the estimate. Using 20 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 .

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> summary(model3)

==========================
Summary of model fit
==========================

Formula:   nomin.short.nw ~ edges + istar(2) + ostar(2:3) + twopath + isolates + 
    nodematch("nationality") + nodematch("employer") + nodeofactor("gender") + 
    nodeifactor("gender") + nodemix("gender", base = -4) + nodeicov("inst.num") + 
    nodeocov("inst.num") + edgecov(inst.cooc) + edgecov(inst.cooc.sq) + 
    nodematch("expertise") + edgecov(chapters.mat.binary) + nodematch("education") + 
    nodeifactor("phd") + nodeofactor("phd") + nodeifactor("socsci") + 
    nodeofactor("socsci") + nodematch("socsci") + nodeofactor("authortype", 
    base = -2) + edgecov(cla.interaction) + nodeofactor("leader") + 
    edgecov(leader.interaction)

Iterations:  4 out of 20 

Monte Carlo MLE Results:
                                                Estimate Std. Error MCMC %
edges                                           -7.99540    0.57181      0
istar2                                          -1.44202    0.33925      0
ostar2                                           0.45072    0.06753      0
ostar3                                          -0.04209    0.01085      0
twopath                                         -0.26480    0.09871      0
isolates                                         0.26839    0.28767      0
nodematch.nationality                            1.67925    0.19276      0
nodematch.employer                               3.17537    0.41906      0
nodeofactor.gender.Male                          0.54498    0.29469      0
nodeifactor.gender.Male                          0.20199    0.37821      0
mix.gender.Male.Male                            -0.48957    0.40474      0
nodeicov.inst.num                               -0.10993    0.05173      0
nodeocov.inst.num                               -0.06434    0.02491      0
edgecov.inst.cooc                                1.40066    0.23290      0
edgecov.inst.cooc.sq                            -0.16929    0.04865      0
nodematch.expertise                              0.61273    0.21869      0
edgecov.chapters.mat.binary                      3.51234    0.18792      0
nodematch.education                             -0.37900    0.19237      0
nodeifactor.phd.1                               -0.04971    0.23241      0
nodeofactor.phd.1                                0.24769    0.16787      0
nodeifactor.socsci.yes                           0.20585    0.23020      0
nodeofactor.socsci.yes                           0.18364    0.14049      0
nodematch.socsci                                 0.12837    0.19448      0
nodeofactor.authortype.coordinating lead author  0.93038    0.19435      0
edgecov.cla.interaction                         -0.13622    0.20577      0
nodeofactor.leader.1                             0.62182    0.22039      0
edgecov.leader.interaction                      -0.04937    0.18984      0
                                                 p-value    
edges                                            < 1e-04 ***
istar2                                           < 1e-04 ***
ostar2                                           < 1e-04 ***
ostar3                                          0.000105 ***
twopath                                         0.007306 ** 
isolates                                        0.350822    
nodematch.nationality                            < 1e-04 ***
nodematch.employer                               < 1e-04 ***
nodeofactor.gender.Male                         0.064409 .  
nodeifactor.gender.Male                         0.593300    
mix.gender.Male.Male                            0.226429    
nodeicov.inst.num                               0.033595 *  
nodeocov.inst.num                               0.009784 ** 
edgecov.inst.cooc                                < 1e-04 ***
edgecov.inst.cooc.sq                            0.000501 ***
nodematch.expertise                             0.005083 ** 
edgecov.chapters.mat.binary                      < 1e-04 ***
nodematch.education                             0.048828 *  
nodeifactor.phd.1                               0.830615    
nodeofactor.phd.1                               0.140087    
nodeifactor.socsci.yes                          0.371216    
nodeofactor.socsci.yes                          0.191163    
nodematch.socsci                                0.509210    
nodeofactor.authortype.coordinating lead author  < 1e-04 ***
edgecov.cla.interaction                         0.507988    
nodeofactor.leader.1                            0.004781 ** 
edgecov.leader.interaction                      0.794806    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 180163  on 129960  degrees of freedom
 Residual Deviance:   1663  on 129933  degrees of freedom
 
AIC: 1717    BIC: 1981    (Smaller is better.) 
> 
> # ERGM: without elite club variables
> model4 <- ergm(nomin.short.nw ~ 
+     edges + 
+     istar(2) + 
+     ostar(2:3) + 
+     twopath + 
+     isolates + 
+     nodematch("nationality") + 
+     nodematch("employer") + 
+     nodeofactor("gender") + 
+     nodeifactor("gender") + 
+     nodemix("gender", base = -4) + 
+     #nodeicov("inst.num") + 
+     #nodeocov("inst.num") + 
+     #edgecov(inst.cooc) + 
+     #edgecov(inst.cooc.sq) + 
+     nodematch("expertise") + 
+     edgecov(chapters.mat.binary) + 
+     nodematch("education") + 
+     nodeifactor("phd") + 
+     nodeofactor("phd") + 
+     nodeifactor("socsci") + 
+     nodeofactor("socsci") + 
+     nodematch("socsci") + 
+     nodeofactor("authortype", base = -2) + 
+     nodeofactor("leader")
+     , control = control.ergm(MCMC.interval = 2000, MCMC.burnin = 30000, 
+     MCMC.samplesize = 15000)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 20: 
The log-likelihood improved by 4.687 
Iteration 2 of at most 20: 
The log-likelihood improved by 3.849 
Iteration 3 of at most 20: 
The log-likelihood improved by 0.8621 
Step length converged once. Increasing MCMC sample size.
Iteration 4 of at most 20: 
The log-likelihood improved by 0.0445 
Step length converged twice. Stopping.
Evaluating log-likelihood at the estimate. Using 20 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 .

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> summary(model4)

==========================
Summary of model fit
==========================

Formula:   nomin.short.nw ~ edges + istar(2) + ostar(2:3) + twopath + isolates + 
    nodematch("nationality") + nodematch("employer") + nodeofactor("gender") + 
    nodeifactor("gender") + nodemix("gender", base = -4) + nodematch("expertise") + 
    edgecov(chapters.mat.binary) + nodematch("education") + nodeifactor("phd") + 
    nodeofactor("phd") + nodeifactor("socsci") + nodeofactor("socsci") + 
    nodematch("socsci") + nodeofactor("authortype", base = -2) + 
    nodeofactor("leader")

Iterations:  4 out of 20 

Monte Carlo MLE Results:
                                                 Estimate Std. Error MCMC %
edges                                           -7.991781   0.570565      0
istar2                                          -1.470378   0.344399      0
ostar2                                           0.477458   0.069559      0
ostar3                                          -0.045492   0.011451      0
twopath                                         -0.240842   0.099004      0
isolates                                         0.270903   0.287698      0
nodematch.nationality                            1.627038   0.192410      0
nodematch.employer                               3.241918   0.413570      0
nodeofactor.gender.Male                          0.456495   0.289725      0
nodeifactor.gender.Male                          0.096777   0.377929      0
mix.gender.Male.Male                            -0.367607   0.401760      0
nodematch.expertise                              0.664029   0.217427      0
edgecov.chapters.mat.binary                      3.641975   0.186314      0
nodematch.education                             -0.349840   0.187235      0
nodeifactor.phd.1                               -0.009374   0.234314      0
nodeofactor.phd.1                                0.263056   0.163517      0
nodeifactor.socsci.yes                           0.158679   0.229482      0
nodeofactor.socsci.yes                           0.208895   0.127152      0
nodematch.socsci                                 0.172755   0.192095      0
nodeofactor.authortype.coordinating lead author  0.802878   0.157113      0
nodeofactor.leader.1                             0.592874   0.170551      0
                                                 p-value    
edges                                            < 1e-04 ***
istar2                                           < 1e-04 ***
ostar2                                           < 1e-04 ***
ostar3                                           < 1e-04 ***
twopath                                         0.014990 *  
isolates                                        0.346388    
nodematch.nationality                            < 1e-04 ***
nodematch.employer                               < 1e-04 ***
nodeofactor.gender.Male                         0.115117    
nodeifactor.gender.Male                         0.797897    
mix.gender.Male.Male                            0.360197    
nodematch.expertise                             0.002258 ** 
edgecov.chapters.mat.binary                      < 1e-04 ***
nodematch.education                             0.061702 .  
nodeifactor.phd.1                               0.968088    
nodeofactor.phd.1                               0.107677    
nodeifactor.socsci.yes                          0.489276    
nodeofactor.socsci.yes                          0.100410    
nodematch.socsci                                0.368485    
nodeofactor.authortype.coordinating lead author  < 1e-04 ***
nodeofactor.leader.1                            0.000509 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 180163  on 129960  degrees of freedom
 Residual Deviance:   1711  on 129939  degrees of freedom
 
AIC: 1753    BIC: 1958    (Smaller is better.) 
> 
> 
> ################################################################################
> # Degeneracy check and goodness-of-fit assessment
> ################################################################################
> 
> pdf("output/mcmc-diagnostics.pdf", width = 10, height = 13)
> par(mfrow = c(7, 4), mar = c(2, 2, 2, 2))
> plot(model3$sample, auto.layout = FALSE, ask = FALSE)
> dev.off()
null device 
          1 
> 
> gof3 <- gof(model3, nsim = 1000, MCMC.interval = 10000, MCMC.burnin = 30000,
+     ncpus = ncpus, parallel = parallel, statistics = c(dsp, esp, ideg, odeg, 
+     geodesic, triad.directed, rocpr), roc = FALSE)

Starting GOF assessment using parallel processing on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 nomin.short.nw ~ edges + istar(2) + ostar(2:3) + twopath + isolates + nodematch("nationality") + nodematch("employer") + nodeofactor("gender") + nodeifactor("gender") + nodemix("gender", base = -4) + nodeicov("inst.num") + nodeocov("inst.num") + edgecov(inst.cooc) + edgecov(inst.cooc.sq) + nodematch("expertise") + edgecov(chapters.mat.binary) + nodematch("education") + nodeifactor("phd") + nodeofactor("phd") + nodeifactor("socsci") + nodeofactor("socsci") + nodematch("socsci") + nodeofactor("authortype", base = -2) + edgecov(cla.interaction) + nodeofactor("leader") + edgecov(leader.interaction) 

One network from which simulations are drawn was provided.

Processing statistic: Dyad-wise shared partners
Processing statistic: Edge-wise shared partners
Processing statistic: Indegree
Processing statistic: Outdegree
Processing statistic: Geodesic distances
Processing statistic: Triad census
Processing statistic: Tie prediction
> gof4 <- gof(model4, nsim = 1000, MCMC.interval = 10000, MCMC.burnin = 30000,
+     ncpus = ncpus, parallel = parallel, statistics = c(rocpr), roc = FALSE)

Starting GOF assessment using parallel processing on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 nomin.short.nw ~ edges + istar(2) + ostar(2:3) + twopath + isolates + nodematch("nationality") + nodematch("employer") + nodeofactor("gender") + nodeifactor("gender") + nodemix("gender", base = -4) + nodematch("expertise") + edgecov(chapters.mat.binary) + nodematch("education") + nodeifactor("phd") + nodeofactor("phd") + nodeifactor("socsci") + nodeofactor("socsci") + nodematch("socsci") + nodeofactor("authortype", base = -2) + nodeofactor("leader") 

One network from which simulations are drawn was provided.

Processing statistic: Tie prediction
> gof.dyadindep <- gof(model2, nsim = 1000, MCMC.interval = 10000, 
+     MCMC.burnin = 30000, ncpus = ncpus, parallel = parallel, 
+     statistics = c(dsp, esp, ideg, odeg, geodesic, triad.directed, rocpr), 
+     roc = FALSE)

Starting GOF assessment using parallel processing on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 nomin.short.nw ~ edges + nodematch("nationality") + nodematch("employer") + nodeofactor("gender") + nodeifactor("gender") + nodemix("gender", base = -4) + nodeicov("inst.num") + nodeocov("inst.num") + edgecov(inst.cooc) + edgecov(inst.cooc.sq) + nodematch("expertise") + edgecov(chapters.mat.binary) + nodematch("education") + nodeifactor("phd") + nodeofactor("phd") + nodeifactor("socsci") + nodeofactor("socsci") + nodematch("socsci") + nodeofactor("authortype", base = -2) + edgecov(cla.interaction) + nodeofactor("leader") + edgecov(leader.interaction) 

One network from which simulations are drawn was provided.

Processing statistic: Dyad-wise shared partners
Processing statistic: Edge-wise shared partners
Processing statistic: Indegree
Processing statistic: Outdegree
Processing statistic: Geodesic distances
Processing statistic: Triad census
Processing statistic: Tie prediction
> 
> gof3boxplots <- gof3[1:6]
> class(gof3boxplots) <- "gof"
> pdf("output/gof-boxplot-full.pdf", width = 10, height = 6)
> plot(gof3boxplots, transform = log1p)
> dev.off()
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          1 
> 
> gof.di.bp <- gof.dyadindep[1:6]
> class(gof.di.bp) <- "gof"
> pdf("output/gof-boxplot-dyadindep.pdf", width = 10, height = 6)
> par(mfrow = c(2, 3))
> plot(gof.di.bp, transform = log1p)
> dev.off()
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          1 
> 
> pdf("output/figure_4.pdf", width = 4, height = 4)
> plot(gof3[[7]], rgraph = TRUE, col = "#000000", random.col = "#E6E6E6")
> plot(gof4, add = TRUE, pr.add = TRUE, col = "#A7A7A7")
> legend(0.1, 0.97, col = c("#000000", "#A7A7A7", "#E6E6E6"), 
+     legend = c("Full model", "Without memberships", "Null model"), 
+     lty = "solid", lwd = 3)
> dev.off()
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          1 
> 
> 
> ################################################################################
> # Regression tables
> ################################################################################
> 
> map <- list("nodeicov.inst.num" = "Institutional memberships (receiver)", 
+             "nodeocov.inst.num" = "Institutional memberships (sender)", 
+             "edgecov.inst.cooc" = "Institutional co-memberships", 
+             "edgecov.inst.cooc.sq" = "Institutional co-memberships$^2$", 
+             "nodematch.nationality" = "Same nationality", 
+             "nodematch.employer" = "Same employer/university affiliation", 
+             "nodeofactor.gender.Male" = "Sender male", 
+             "nodeifactor.gender.Male" = "Receiver male", 
+             "mix.gender.Male.Male" = "Sender male, receiver male", 
+             "nodematch.expertise" = "Same area of expertise", 
+             "edgecov.chapters.mat.binary" = "Joint chapter(s) in the report", 
+             "nodematch.education" = "Same type and level of degree", 
+             "nodeifactor.phd.1" = "Receiver has a PhD or MD", 
+             "nodeofactor.phd.1" = "Sender has a PhD or MD", 
+             "nodeifactor.socsci.yes" = "Receiver is a social scientist", 
+             "nodeofactor.socsci.yes" = "Sender is a social scientist", 
+             "nodematch.socsci" = "Both are social/natural scientists", 
+             "nodeofactor.authortype.coordinating lead author" = "Sender is a CLA", 
+             "edgecov.cla.interaction" = "Sender CLA $\\times$ Institutional co-memberships", 
+             "nodeofactor.leader.1" = "Sender is a Leader", 
+             "edgecov.leader.interaction" = "Leader $\\times$ Institutional co-memberships", 
+             "edges" = "Edges", 
+             "istar2" = "Two-stars (incoming)", 
+             "ostar2" = "Two-stars (outgoing)", 
+             "ostar3" = "Three-stars (outgoing)", 
+             "twopath" = "Two-paths", 
+             "isolates" = "Isolates"
+             )
> 
> screenreg(model3, single.row = TRUE, custom.model.names = "ERGM", 
+     groups = list("Institutional elite memberships" = 1:4, 
+     "Exogenous controls" = 5:21, "Endogenous dependencies" = 22:27), 
+     custom.coef.map = map, include.aic = FALSE, include.bic = FALSE, 
+     include.loglik = FALSE, file = "output/table.txt"
+ )
The table was written to the file 'output/table.txt'.

> 
> texreg(model3, single.row = TRUE, custom.model.names = "ERGM", 
+     groups = list("Institutional elite memberships" = 1:4, 
+     "Exogenous controls" = 5:21, "Endogenous dependencies" = 22:27), 
+     custom.coef.map = map, include.aic = FALSE, include.bic = FALSE, 
+     include.loglik = FALSE, file = "output/table.tex", booktabs = TRUE, 
+     dcolumn = TRUE, use.packages = FALSE, caption = "ERGM results"
+ )
The table was written to the file 'output/table.tex'.

> 
> htmlreg(model3, single.row = TRUE, custom.model.names = "ERGM", 
+     groups = list("Institutional elite memberships" = 1:4, 
+     "Exogenous controls" = 5:21, "Endogenous dependencies" = 22:27), 
+     custom.coef.map = map, include.aic = FALSE, include.bic = FALSE, 
+     include.loglik = FALSE, file = "output/table.html", 
+     caption = "ERGM results"
+ )
The table was written to the file 'output/table.html'.

> 
> # save everything to a file for later use
> save(list = ls(), file = "output/workspace.RData")
> 
> 
> ################################################################################
> # Marginal effects plot
> ################################################################################
> 
> ep <- edgeprob(model3)
> 
> mp <- marginalplot(model3, 
+                    var1 = "edgecov.inst.cooc", 
+                    var2 = "edgecov.inst.cooc", 
+                    inter = "edgecov.inst.cooc.sq", 
+                    xlab = "Institutional co-memberships (conditioning variable)", 
+                    ylab = "Institutional co-memberships (coefficient)") + 
+   theme_bw() + 
+   labs(title = "Marginal effects for squared model term")
> 
> tab <- data.frame(table(ep$`edgecov.inst.cooc[[i]]`))
> fr <- ggplot(tab[tab$Var1 != 0, ], aes(Var1, Freq)) + 
+   geom_bar(stat = "identity") + 
+   theme_bw() + 
+   xlab("Institutional co-memberships") + 
+   ylab("Frequency") + 
+   labs(title = "Frequency and range of institutional co-memberships (> 0)")
> 
> pp <- ggplot(ep[ep$`edgecov.inst.cooc[[i]]` %in% 0:4, ], 
+        aes(x = `edgecov.inst.cooc[[i]]`, y = probability)) + 
+   theme_bw() + 
+   stat_summary(geom = "ribbon", 
+                fun.data = mean_cl_normal, 
+                fun.args = (conf.int=1), 
+                fill = "lightgray") + 
+   stat_summary(geom = "line", fun.y = mean) + 
+   ylab("Predicted probability") +
+   xlab("Institutional co-memberships") + 
+   labs(title = "Predicted probabilities for highest-frequency values")
> 
> require("gridExtra")
Loading required package: gridExtra
> pdf("output/interpretation.pdf", width = 18, height = 6)
> grid.arrange(mp, fr, pp, ncol = 3)
> dev.off()
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> 
> pdf("output/marginaleffect.pdf")
> plot(mp)
> dev.off()
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> 
> pdf("output/predictedprob.pdf")
> plot(pp)
> dev.off()
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> 
> pdf("output/instfreq.pdf")
> plot(fr)
> dev.off()
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> 
> proc.time()
    user   system  elapsed 
1116.784    1.040 1571.865 
